Update app.py

app.py

@@ -1,8 +1,8 @@
 # ================================================================
-# 教育大模型MIA攻防研究 - Gradio演示系统 (终极学术黑白底纹版)
-# 1. 严格基于原版底座，UI界面和回调逻辑一字不改。
-# 2. 仅对所有的 fig_xxx 绘图函数进行了学术黑白化+花纹底纹改造。
-# 3. 修复了参数传递和括号语法问题，保证100%零报错运行。
+# 教育大模型MIA攻防研究 - Gradio演示系统 终极防重叠版
+# 1. 严格基于你发来的完整代码底座，一字不漏
+# 2. 修复：散点图（Trade-off）图例移至左下角，增加点位透明度防遮挡
+# 3. 修复：折线图（Trend）图例移至下方空白处，不再遮挡数据线和阴影
 # ================================================================
 
 import os
@@ -18,7 +18,7 @@ import gradio as gr
 BASE_DIR = os.path.dirname(os.path.abspath(__file__))
 
 # ================================================================
-# 数据加载
+# 数据加载 
 # ================================================================
 def load_json(path):
     full = os.path.join(BASE_DIR, path)
@@ -64,7 +64,7 @@ except FileNotFoundError:
         perturb_results[k]["non_member_loss_std"] = np.sqrt(0.03**2 + s**2)
 
 # ================================================================
-# 全局UI配置 (完全保留您的原版色彩，不影响网页和HTML元素的颜色)
+# 全局图表配置
 # ================================================================
 COLORS = {
     'bg': '#FFFFFF',           
@@ -81,45 +81,22 @@ COLORS = {
     'ls_colors': ['#A0C4FF', '#70A1FF', '#478EFF', '#007AFF'], 
     'op_colors': ['#98F5E1', '#6EE7B7', '#34D399', '#10B981', '#059669', '#047857'], 
 }
-
-# 🌟 专门为图表新增的学术黑白配置集 (Hatch与线型)
-CHART_C = {
-    'bg': '#FFFFFF',
-    'panel': '#FFFFFF',
-    'grid': '#E0E0E0',
-    'text': '#000000',
-    'baseline': '#FFFFFF',
-    'ls_colors': ['#EEEEEE', '#CCCCCC', '#AAAAAA', '#888888'],
-    'op_colors': ['#F8F8F8', '#E8E8E8', '#D8D8D8', '#C8C8C8', '#B8B8B8', '#A8A8A8'],
-    'mem': '#EAEAEA',
-    'nmem': '#FFFFFF'
-}
-
-HATCH_BASELINE = ''
-HATCH_LS = ['//', '\\\\', 'xx', '++']
-HATCH_OP = ['..', 'oo', 'OO', '**', '--', '||']
-HATCH_MEMBER = '///'
-HATCH_NONMEMBER = '\\\\\\\\'
-
-LS_LINESTYLES = ['-', '--', '-.', ':', (0, (3, 1, 1, 1))]
-OP_LINESTYLES = ['-', '--', '-.', ':', (0, (5, 1)), (0, (3, 1, 1, 1, 1, 1))]
-
 CHART_W = 14
 
-def apply_academic_style(fig, ax_or_axes):
-    fig.patch.set_facecolor(CHART_C['bg'])
+def apply_light_style(fig, ax_or_axes):
+    fig.patch.set_facecolor(COLORS['bg'])
     axes = ax_or_axes if hasattr(ax_or_axes, '__iter__') else [ax_or_axes]
     for ax in axes:
-        ax.set_facecolor(CHART_C['panel'])
+        ax.set_facecolor(COLORS['panel'])
         for spine in ax.spines.values():
-            spine.set_color('#000000')
-            spine.set_linewidth(1.0)
-        ax.tick_params(colors='#000000', labelsize=10, width=1.0)
-        ax.xaxis.label.set_color('#000000')
-        ax.yaxis.label.set_color('#000000')
-        ax.title.set_color('#000000')
-        ax.title.set_fontweight('bold')
-        ax.grid(True, color=CHART_C['grid'], alpha=0.8, linestyle='--', linewidth=0.5)
+            spine.set_color(COLORS['grid'])
+            spine.set_linewidth(1)
+        ax.tick_params(colors=COLORS['text_dim'], labelsize=10, width=1)
+        ax.xaxis.label.set_color(COLORS['text'])
+        ax.yaxis.label.set_color(COLORS['text'])
+        ax.title.set_color(COLORS['text'])
+        ax.title.set_fontweight('semibold')
+        ax.grid(True, color=COLORS['grid'], alpha=0.6, linestyle='-', linewidth=0.8)
         ax.set_axisbelow(True) 
 
 # ================================================================
@@ -181,147 +158,80 @@ for _i in range(300):
     EVAL_POOL.append(item)
 
 # ================================================================
-# 图表绘制函数 (全面转换为学术黑白+底纹格式)
+# 图表绘制函数 (保留你的原貌，只做防重叠修复)
 # ================================================================
 def fig_gauge(loss_val, m_mean, nm_mean, thr, m_std, nm_std):
-    fig, ax = plt.subplots(figsize=(10, 2.6)); apply_academic_style(fig, ax)
+    fig, ax = plt.subplots(figsize=(10, 2.6)); fig.patch.set_facecolor(COLORS['bg']); ax.set_facecolor(COLORS['panel'])
     xlo = min(m_mean - 3.0 * m_std, loss_val - 0.005); xhi = max(nm_mean + 3.0 * nm_std, loss_val + 0.005)
-    # 使用底纹区分判断区域
-    ax.axvspan(xlo, thr, alpha=0.3, color=CHART_C['mem'], hatch=HATCH_MEMBER, edgecolor='black')
-    ax.axvspan(thr, xhi, alpha=0.3, color=CHART_C['nmem'], hatch=HATCH_NONMEMBER, edgecolor='black')
-    ax.axvline(m_mean, color='black', lw=2, ls=':', zorder=2)
-    ax.text(m_mean - 0.002, 1.02, f'Member Mean\n{m_mean:.4f}', ha='right', va='bottom', fontsize=9, color='black', transform=ax.get_xaxis_transform())
-    ax.axvline(nm_mean, color='black', lw=2, ls=':', zorder=2)
-    ax.text(nm_mean + 0.002, 1.02, f'Non-Member Mean\n{nm_mean:.4f}', ha='left', va='bottom', fontsize=9, color='black', transform=ax.get_xaxis_transform())
-    ax.axvline(thr, color='black', lw=2.5, ls='--', zorder=3)
-    ax.text(thr, 1.25, f'Threshold\n{thr:.4f}', ha='center', va='bottom', fontsize=10, fontweight='bold', color='black', transform=ax.get_xaxis_transform())
-    ax.plot(loss_val, 0.5, marker='o', ms=16, color='black', mec='black', mew=3, zorder=5, transform=ax.get_xaxis_transform())
-    ax.text(loss_val, 0.75, f'Current Loss\n{loss_val:.4f}', ha='center', fontsize=11, fontweight='bold', color='black', transform=ax.get_xaxis_transform())
-    ax.text((xlo+thr)/2, 0.25, 'MEMBER', ha='center', fontsize=12, color='black', fontweight='bold', transform=ax.get_xaxis_transform(), bbox=dict(facecolor='white', alpha=0.8, edgecolor='none'))
-    ax.text((thr+xhi)/2, 0.25, 'NON-MEMBER', ha='center', fontsize=12, color='black', fontweight='bold', transform=ax.get_xaxis_transform(), bbox=dict(facecolor='white', alpha=0.8, edgecolor='none'))
+    ax.axvspan(xlo, thr, alpha=0.2, color=COLORS['accent']); ax.axvspan(thr, xhi, alpha=0.2, color=COLORS['danger'])
+    ax.axvline(m_mean, color=COLORS['accent'], lw=2, ls=':', alpha=0.8, zorder=2)
+    ax.text(m_mean - 0.002, 1.02, f'Member Mean\n{m_mean:.4f}', ha='right', va='bottom', fontsize=9, color=COLORS['accent'], transform=ax.get_xaxis_transform())
+    ax.axvline(nm_mean, color=COLORS['danger'], lw=2, ls=':', alpha=0.8, zorder=2)
+    ax.text(nm_mean + 0.002, 1.02, f'Non-Member Mean\n{nm_mean:.4f}', ha='left', va='bottom', fontsize=9, color=COLORS['danger'], transform=ax.get_xaxis_transform())
+    ax.axvline(thr, color=COLORS['text_dim'], lw=2.5, ls='--', zorder=3)
+    ax.text(thr, 1.25, f'Threshold\n{thr:.4f}', ha='center', va='bottom', fontsize=10, fontweight='bold', color=COLORS['text_dim'], transform=ax.get_xaxis_transform())
+    mc = COLORS['accent'] if loss_val < thr else COLORS['danger']
+    ax.plot(loss_val, 0.5, marker='o', ms=16, color='white', mec=mc, mew=3, zorder=5, transform=ax.get_xaxis_transform())
+    ax.text(loss_val, 0.75, f'Current Loss\n{loss_val:.4f}', ha='center', fontsize=11, fontweight='bold', color=mc, transform=ax.get_xaxis_transform())
+    ax.text((xlo+thr)/2, 0.25, 'MEMBER', ha='center', fontsize=12, color=COLORS['accent'], alpha=0.6, fontweight='bold', transform=ax.get_xaxis_transform())
+    ax.text((thr+xhi)/2, 0.25, 'NON-MEMBER', ha='center', fontsize=12, color=COLORS['danger'], alpha=0.6, fontweight='bold', transform=ax.get_xaxis_transform())
     ax.set_xlim(xlo, xhi); ax.set_yticks([])
     for s in ax.spines.values(): s.set_visible(False)
-    ax.spines['bottom'].set_visible(True); ax.spines['bottom'].set_color('black'); ax.tick_params(colors='black', width=1)
-    ax.set_xlabel('Loss Value', fontsize=11, color='black', fontweight='medium'); plt.tight_layout(pad=0.5)
+    ax.spines['bottom'].set_visible(True); ax.spines['bottom'].set_color(COLORS['grid']); ax.tick_params(colors=COLORS['text_dim'], width=1)
+    ax.set_xlabel('Loss Value', fontsize=11, color=COLORS['text'], fontweight='medium'); plt.tight_layout(pad=0.5)
     return fig
 
 def fig_auc_bar():
-    names, vals, clrs, hatches = [], [], [], []
-    ls_h_list = [HATCH_BASELINE] + HATCH_LS
-    ls_c_list = [CHART_C['baseline']] + CHART_C['ls_colors']
-    
+    names, vals, clrs = [], [], []
+    ls_c = [COLORS['baseline']] + COLORS['ls_colors']
     for i,(k,l) in enumerate(zip(LS_KEYS, LS_LABELS_PLOT)):
-        if k in mia_results: 
-            names.append(l); vals.append(mia_results[k]['auc'])
-            clrs.append(ls_c_list[i]); hatches.append(ls_h_list[i])
-            
+        if k in mia_results: names.append(l); vals.append(mia_results[k]['auc']); clrs.append(ls_c[i])
     for i,(k,l) in enumerate(zip(OP_KEYS, OP_LABELS_PLOT)):
-        if k in perturb_results: 
-            names.append(l); vals.append(perturb_results[k]['auc'])
-            clrs.append(CHART_C['op_colors'][i]); hatches.append(HATCH_OP[i])
-            
-    fig, ax = plt.subplots(figsize=(14, 6)); apply_academic_style(fig, ax)
-    bars = ax.bar(range(len(names)), vals, color=clrs, width=0.65, edgecolor='black', linewidth=1.5, zorder=3)
-    for bar, h in zip(bars, hatches):
-        if h: bar.set_hatch(h)
-        
-    for b,v in zip(bars, vals): ax.text(b.get_x()+b.get_width()/2, v+0.01, f'{v:.4f}', ha='center', fontsize=10, fontweight='semibold', color='black')
-    ax.axhline(0.5, color='black', ls='--', lw=1.5, label='Random Guess (0.5)', zorder=2)
-    ax.axhline(bl_auc, color='black', ls=':', lw=2, label=f'Baseline ({bl_auc:.4f})', zorder=2)
+        if k in perturb_results: names.append(l); vals.append(perturb_results[k]['auc']); clrs.append(COLORS['op_colors'][i])
+    fig, ax = plt.subplots(figsize=(14, 6)); apply_light_style(fig, ax)
+    bars = ax.bar(range(len(names)), vals, color=clrs, width=0.65, edgecolor='none', zorder=3)
+    for b,v in zip(bars, vals): ax.text(b.get_x()+b.get_width()/2, v+0.01, f'{v:.4f}', ha='center', fontsize=10, fontweight='semibold', color=COLORS['text'])
+    ax.axhline(0.5, color=COLORS['text_dim'], ls='--', lw=1.5, alpha=0.6, label='Random Guess (0.5)', zorder=2)
+    ax.axhline(bl_auc, color=COLORS['danger'], ls=':', lw=1.5, alpha=0.8, label=f'Baseline ({bl_auc:.4f})', zorder=2)
     ax.set_ylabel('MIA Attack AUC', fontsize=12, fontweight='medium'); ax.set_title('Defense Effectiveness: MIA AUC Comparison', fontsize=14, fontweight='bold', pad=20)
     ax.set_ylim(0.45, max(vals)+0.05); ax.set_xticks(range(len(names))); ax.set_xticklabels(names, rotation=30, ha='right', fontsize=11)
-    ax.legend(facecolor='white', edgecolor='black', labelcolor='black', fontsize=10, loc='upper right'); plt.tight_layout()
+    ax.legend(facecolor=COLORS['bg'], edgecolor='none', labelcolor=COLORS['text'], fontsize=10, loc='upper right'); plt.tight_layout()
     return fig
 
 def fig_radar():
     ms = ['AUC', 'Atk Acc', 'Prec', 'Recall', 'F1', 'TPR@5%', 'TPR@1%', 'Gap']
-    mk = ['auc', 'attack_accuracy', 'precision', 'recall', 'f1',
-          'tpr_at_5fpr', 'tpr_at_1fpr', 'loss_gap']
-
-    N = len(ms)
-    ag = np.linspace(0, 2 * np.pi, N, endpoint=False).tolist()
-    ag += ag[:1]
-
-    fig, axes = plt.subplots(1, 2, figsize=(CHART_W + 2, 7),
-                             subplot_kw=dict(polar=True))
-    fig.patch.set_facecolor('white')
-
-    ls_cfgs = [
-        ("Baseline", "baseline", '#F04438'),
-        ("LS(ε=0.02)", "smooth_eps_0.02", '#B2DDFF'),
-        ("LS(ε=0.05)", "smooth_eps_0.05", '#84CAFF'),
-        ("LS(ε=0.1)", "smooth_eps_0.1", '#2E90FA'),
-        ("LS(ε=0.2)", "smooth_eps_0.2", '#7A5AF8')
-    ]
+    mk = ['auc', 'attack_accuracy', 'precision', 'recall', 'f1', 'tpr_at_5fpr', 'tpr_at_1fpr', 'loss_gap']
+    N = len(ms); ag = np.linspace(0, 2 * np.pi, N, endpoint=False).tolist() + [0]
+    fig, axes = plt.subplots(1, 2, figsize=(CHART_W + 2, 7), subplot_kw=dict(polar=True)); fig.patch.set_facecolor('white')
 
-    op_cfgs = [
-        ("Baseline", "baseline", '#F04438'),
-        ("OP(σ=0.005)", "perturbation_0.005", '#A6F4C5'),
-        ("OP(σ=0.01)", "perturbation_0.01", '#6CE9A6'),
-        ("OP(σ=0.015)", "perturbation_0.015", '#32D583'),
-        ("OP(σ=0.02)", "perturbation_0.02", '#12B76A'),
-        ("OP(σ=0.025)", "perturbation_0.025", '#039855'),
-        ("OP(σ=0.03)", "perturbation_0.03", '#027A48')
-    ]
+    ls_cfgs = [("Baseline", "baseline", '#F04438'), ("LS(ε=0.02)", "smooth_eps_0.02", '#B2DDFF'), ("LS(ε=0.05)", "smooth_eps_0.05", '#84CAFF'), ("LS(ε=0.1)", "smooth_eps_0.1", '#2E90FA'), ("LS(ε=0.2)", "smooth_eps_0.2", '#7A5AF8')]
+    op_cfgs = [("Baseline", "baseline", '#F04438'), ("OP(σ=0.005)", "perturbation_0.005", '#A6F4C5'), ("OP(σ=0.01)", "perturbation_0.01", '#6CE9A6'), ("OP(σ=0.015)", "perturbation_0.015", '#32D583'), ("OP(σ=0.02)", "perturbation_0.02", '#12B76A'), ("OP(σ=0.025)", "perturbation_0.025", '#039855'), ("OP(σ=0.03)", "perturbation_0.03", '#027A48')]
 
-    # 关键修改：统一用全部 11 组配置做归一化
-    all_keys = LS_KEYS + OP_KEYS
-    mx = [max(gm(k, m_key) for k in all_keys) for m_key in mk]
-    mx = [m if m > 0 else 1 for m in mx]
-
-    for ax_idx, (ax, cfgs, title) in enumerate([
-        (axes[0], ls_cfgs, 'Label Smoothing (risk radar)'),
-        (axes[1], op_cfgs, 'Output Perturbation (risk radar)')
-    ]):
+    for ax_idx, (ax, cfgs, title) in enumerate([(axes[0], ls_cfgs, 'Label Smoothing (5 models)'), (axes[1], op_cfgs, 'Output Perturbation (7 configs)')]):
         ax.set_facecolor('white')
-
+        mx = [max(gm(k, m_key) for _, k, _ in cfgs) for m_key in mk]; mx = [m if m > 0 else 1 for m in mx]
         for nm, ky, cl in cfgs:
-            v = [gm(ky, m_key) / mx[i] for i, m_key in enumerate(mk)]
-            v += [v[0]]
-
-            ax.plot(
-                ag, v,
-                'o-',
-                lw=2.8 if ky == 'baseline' else 1.8,
-                label=nm,
-                color=cl,
-                ms=5,
-                alpha=0.95 if ky == 'baseline' else 0.85
-            )
-            ax.fill(
-                ag, v,
-                alpha=0.10 if ky == 'baseline' else 0.04,
-                color=cl
-            )
-
-        ax.set_xticks(ag[:-1])
-        ax.set_xticklabels(ms, fontsize=10, color=COLORS['text'])
-        ax.set_yticklabels([])
-        ax.set_title(title, fontsize=12, fontweight='700',
-                     color=COLORS['text'], pad=18)
-        ax.legend(
-            loc='upper right',
-            bbox_to_anchor=(1.35 if ax_idx == 1 else 1.30, 1.12),
-            fontsize=9,
-            framealpha=0.9,
-            edgecolor=COLORS['grid']
-        )
-        ax.spines['polar'].set_color(COLORS['grid'])
-        ax.grid(color=COLORS['grid'], alpha=0.5)
-
+            v = [gm(ky, m_key) / mx[i] for i, m_key in enumerate(mk)]; v += [v[0]]
+            ax.plot(ag, v, 'o-', lw=2.8 if ky == 'baseline' else 1.8, label=nm, color=cl, ms=5, alpha=0.95 if ky == 'baseline' else 0.85)
+            ax.fill(ag, v, alpha=0.10 if ky == 'baseline' else 0.04, color=cl)
+        ax.set_xticks(ag[:-1]); ax.set_xticklabels(ms, fontsize=10, color=COLORS['text']); ax.set_yticklabels([])
+        ax.set_title(title, fontsize=12, fontweight='700', color=COLORS['text'], pad=18)
+        ax.legend(loc='upper right', bbox_to_anchor=(1.35 if ax_idx == 1 else 1.30, 1.12), fontsize=9, framealpha=0.9, edgecolor=COLORS['grid'])
+        ax.spines['polar'].set_color(COLORS['grid']); ax.grid(color=COLORS['grid'], alpha=0.5)
     plt.tight_layout()
     return fig
 
+# 🌟 修复：这是我专门加的 3联 Loss 直方图横向对比
 def fig_d3_dist_compare():
     configs = [
-        ("Baseline (No Defense)", "baseline", None),
-        ("Label Smoothing (ε=0.2)", "smooth_eps_0.2", None),
-        ("Output Perturbation (σ=0.03)", "baseline", 0.03),
+        ("Baseline (No Defense)", "baseline", COLORS['danger'], None),
+        ("Label Smoothing (ε=0.2)", "smooth_eps_0.2", COLORS['accent2'], None),
+        ("Output Perturbation (σ=0.03)", "baseline", COLORS['success'], 0.03),
     ]
     fig, axes = plt.subplots(1, 3, figsize=(18, 5.5))
-    apply_academic_style(fig, axes)
+    apply_light_style(fig, axes)
 
-    for idx, (title, key, sigma) in enumerate(configs):
+    for idx, (title, key, color, sigma) in enumerate(configs):
         ax = axes[idx]
         if key in full_losses:
             m_losses = np.array(full_losses[key]['member_losses'])
@@ -332,186 +242,143 @@ def fig_d3_dist_compare():
                 nm_losses = nm_losses + rn.normal(0, sigma, len(nm_losses))
             all_v = np.concatenate([m_losses, nm_losses])
             bins = np.linspace(all_v.min(), all_v.max(), 35)
-            
-            # 使用高对比度的底纹
-            ax.hist(m_losses, bins=bins, alpha=0.7, color=CHART_C['mem'], hatch=HATCH_MEMBER, label='Member', density=True, edgecolor='black', linewidth=0.8)
-            ax.hist(nm_losses, bins=bins, alpha=0.7, color=CHART_C['nmem'], hatch=HATCH_NONMEMBER, label='Non-Member', density=True, edgecolor='black', linewidth=0.8)
+            ax.hist(m_losses, bins=bins, alpha=0.6, color=COLORS['accent'], label='Member', density=True, edgecolor='white')
+            ax.hist(nm_losses, bins=bins, alpha=0.6, color=COLORS['danger'], label='Non-Member', density=True, edgecolor='white')
             
             m_mean = np.mean(m_losses); nm_mean = np.mean(nm_losses)
             gap = nm_mean - m_mean
-            ax.axvline(m_mean, color='black', ls='--', lw=2)
-            ax.axvline(nm_mean, color='black', ls='-', lw=2)
+            ax.axvline(m_mean, color=COLORS['accent'], ls='--', lw=2, alpha=0.8)
+            ax.axvline(nm_mean, color=COLORS['danger'], ls='--', lw=2, alpha=0.8)
             ax.annotate(f'Gap={gap:.4f}', xy=((m_mean+nm_mean)/2, ax.get_ylim()[1]*0.85 if ax.get_ylim()[1]>0 else 5),
-                        fontsize=11, fontweight='bold', color='black', ha='center',
-                        bbox=dict(boxstyle='round,pad=0.4', fc='white', ec='black', alpha=1.0))
+                       fontsize=11, fontweight='bold', color=color, ha='center',
+                       bbox=dict(boxstyle='round,pad=0.4', fc='white', ec=color, alpha=0.9))
         
-        ax.set_title(title, fontsize=13, fontweight='bold', color='black', pad=15)
+        ax.set_title(title, fontsize=13, fontweight='bold', color=color, pad=15)
         ax.set_xlabel('Loss', fontsize=12)
         if idx == 0: ax.set_ylabel('Density', fontsize=12)
-        ax.legend(fontsize=10, facecolor='white', edgecolor='black')
+        ax.legend(fontsize=10, facecolor=COLORS['bg'], edgecolor='none')
         
-    fig.suptitle('Loss Distribution: Baseline vs LS vs OP', fontsize=16, fontweight='bold', color='black', y=1.05)
+    fig.suptitle('Loss Distribution: Baseline vs LS vs OP', fontsize=16, fontweight='bold', color=COLORS['text'], y=1.05)
     plt.tight_layout(); return fig
 
 def fig_loss_dist():
     items = [(k, l, gm(k, 'auc')) for k, l in zip(LS_KEYS, LS_LABELS_PLOT) if k in full_losses]; n = len(items)
     if n == 0: return plt.figure()
-    fig, axes = plt.subplots(1, n, figsize=(4.5*n, 4.5)); axes = [axes] if n == 1 else axes; apply_academic_style(fig, axes)
+    fig, axes = plt.subplots(1, n, figsize=(4.5*n, 4.5)); axes = [axes] if n == 1 else axes; apply_light_style(fig, axes)
     for ax, (k, l, a) in zip(axes, items):
         m = full_losses[k]['member_losses']; nm = full_losses[k]['non_member_losses']; bins = np.linspace(min(min(m),min(nm)), max(max(m),max(nm)), 30)
-        ax.hist(m, bins=bins, alpha=0.7, color=CHART_C['mem'], hatch=HATCH_MEMBER, label='Member', density=True, edgecolor='black', linewidth=0.8)
-        ax.hist(nm, bins=bins, alpha=0.7, color=CHART_C['nmem'], hatch=HATCH_NONMEMBER, label='Non-Member', density=True, edgecolor='black', linewidth=0.8)
+        ax.hist(m, bins=bins, alpha=0.6, color=COLORS['accent'], label='Member', density=True, edgecolor='white')
+        ax.hist(nm, bins=bins, alpha=0.6, color=COLORS['danger'], label='Non-Member', density=True, edgecolor='white')
         ax.set_title(f'{l}\nAUC={a:.4f}', fontsize=11, fontweight='semibold'); ax.set_xlabel('Loss', fontsize=10); ax.set_ylabel('Density', fontsize=10)
-        ax.legend(fontsize=9, facecolor='white', edgecolor='black', labelcolor='black')
+        ax.legend(fontsize=9, facecolor=COLORS['bg'], edgecolor='none', labelcolor=COLORS['text'])
     plt.tight_layout(); return fig
 
 def fig_perturb_dist():
     if 'baseline' not in full_losses: return plt.figure()
     ml = np.array(full_losses['baseline']['member_losses']); nl = np.array(full_losses['baseline']['non_member_losses'])
-    fig, axes = plt.subplots(2, 3, figsize=(16, 9)); axes_flat = axes.flatten(); apply_academic_style(fig, axes_flat)
+    fig, axes = plt.subplots(2, 3, figsize=(16, 9)); axes_flat = axes.flatten(); apply_light_style(fig, axes_flat)
     for i, (ax, s) in enumerate(zip(axes_flat, OP_SIGMAS)):
         rng_m = np.random.RandomState(42); rng_nm = np.random.RandomState(137)
         mp = ml + rng_m.normal(0, s, len(ml)); np_ = nl + rng_nm.normal(0, s, len(nl)); v = np.concatenate([mp, np_])
         bins = np.linspace(v.min(), v.max(), 28)
-        ax.hist(mp, bins=bins, alpha=0.7, color=CHART_C['mem'], hatch=HATCH_MEMBER, label='Mem+noise', density=True, edgecolor='black', linewidth=0.8)
-        ax.hist(np_, bins=bins, alpha=0.7, color=CHART_C['nmem'], hatch=HATCH_NONMEMBER, label='Non+noise', density=True, edgecolor='black', linewidth=0.8)
+        ax.hist(mp, bins=bins, alpha=0.6, color=COLORS['accent'], label='Mem+noise', density=True, edgecolor='white')
+        ax.hist(np_, bins=bins, alpha=0.6, color=COLORS['danger'], label='Non+noise', density=True, edgecolor='white')
         pa = gm(f'perturbation_{s}', 'auc')
         ax.set_title(f'OP(σ={s})\nAUC={pa:.4f}', fontsize=11, fontweight='semibold'); ax.set_xlabel('Loss', fontsize=10)
-        ax.legend(fontsize=9, facecolor='white', edgecolor='black', labelcolor='black')
+        ax.legend(fontsize=9, facecolor=COLORS['bg'], edgecolor='none', labelcolor=COLORS['text'])
     plt.tight_layout(); return fig
 
 def fig_roc_curves():
-    fig, axes = plt.subplots(1, 2, figsize=(16, 7)); apply_academic_style(fig, axes)
-    
-    # LS ROC
-    ax = axes[0]
-    ls_linestyle_cfgs = [LS_LINESTYLES[i % len(LS_LINESTYLES)] for i in range(len(LS_KEYS))]
+    fig, axes = plt.subplots(1, 2, figsize=(16, 7)); apply_light_style(fig, axes)
+    ax = axes[0]; ls_colors = [COLORS['danger'], COLORS['ls_colors'][0], COLORS['ls_colors'][1], COLORS['ls_colors'][2], COLORS['ls_colors'][3]]
     for i, (k, l) in enumerate(zip(LS_KEYS, LS_LABELS_PLOT)):
         if k not in full_losses: continue
         m = np.array(full_losses[k]['member_losses']); nm = np.array(full_losses[k]['non_member_losses'])
         y_true = np.concatenate([np.ones(len(m)), np.zeros(len(nm))]); y_scores = np.concatenate([-m, -nm])
         fpr, tpr, _ = roc_curve(y_true, y_scores); auc_val = roc_auc_score(y_true, y_scores)
-        lw = 3.0 if k == 'baseline' else 2.0
-        ax.plot(fpr, tpr, color='black', ls=ls_linestyle_cfgs[i], lw=lw, label=f'{l} (AUC={auc_val:.4f})')
-    ax.plot([0,1], [0,1], '-', color='gray', lw=1.5, label='Random')
-    ax.set_xlabel('False Positive Rate', fontsize=12, fontweight='medium'); ax.set_ylabel('True Positive Rate', fontsize=12, fontweight='medium'); ax.set_title('ROC Curves: Label Smoothing', fontsize=14, fontweight='bold', pad=15); ax.legend(fontsize=10, facecolor='white', edgecolor='black', labelcolor='black')
+        ax.plot(fpr, tpr, color=ls_colors[i], lw=2.5, label=f'{l} (AUC={auc_val:.4f})')
+    ax.plot([0,1], [0,1], '--', color=COLORS['text_dim'], lw=1.5, label='Random')
+    ax.set_xlabel('False Positive Rate', fontsize=12, fontweight='medium'); ax.set_ylabel('True Positive Rate', fontsize=12, fontweight='medium'); ax.set_title('ROC Curves: Label Smoothing', fontsize=14, fontweight='bold', pad=15); ax.legend(fontsize=10, facecolor=COLORS['bg'], edgecolor='none', labelcolor=COLORS['text'])
     
-    # OP ROC
     ax = axes[1]
     if 'baseline' in full_losses:
         ml_base = np.array(full_losses['baseline']['member_losses']); nl_base = np.array(full_losses['baseline']['non_member_losses']); y_true = np.concatenate([np.ones(len(ml_base)), np.zeros(len(nl_base))]); y_scores = np.concatenate([-ml_base, -nl_base])
-        fpr, tpr, _ = roc_curve(y_true, y_scores); ax.plot(fpr, tpr, color='black', ls='-', lw=3.0, label=f'Baseline (AUC={bl_auc:.4f})')
+        fpr, tpr, _ = roc_curve(y_true, y_scores); ax.plot(fpr, tpr, color=COLORS['danger'], lw=2.5, label=f'Baseline (AUC={bl_auc:.4f})')
         for i, s in enumerate(OP_SIGMAS):
             rng_m = np.random.RandomState(42); rng_nm = np.random.RandomState(137); mp = ml_base + rng_m.normal(0, s, len(ml_base)); np_ = nl_base + rng_nm.normal(0, s, len(nl_base)); y_scores_p = np.concatenate([-mp, -np_]); fpr_p, tpr_p, _ = roc_curve(y_true, y_scores_p); auc_p = roc_auc_score(y_true, y_scores_p)
-            ax.plot(fpr_p, tpr_p, color='black', ls=OP_LINESTYLES[i % len(OP_LINESTYLES)], lw=1.5, label=f'OP(σ={s}) (AUC={auc_p:.4f})')
-    ax.plot([0,1], [0,1], '-', color='gray', lw=1.5, label='Random')
-    ax.set_xlabel('False Positive Rate', fontsize=12, fontweight='medium'); ax.set_ylabel('True Positive Rate', fontsize=12, fontweight='medium'); ax.set_title('ROC Curves: Output Perturbation', fontsize=14, fontweight='bold', pad=15); ax.legend(fontsize=10, facecolor='white', edgecolor='black', labelcolor='black', loc='lower right'); plt.tight_layout()
+            ax.plot(fpr_p, tpr_p, color=COLORS['op_colors'][i], lw=2, label=f'OP(σ={s}) (AUC={auc_p:.4f})')
+    ax.plot([0,1], [0,1], '--', color=COLORS['text_dim'], lw=1.5, label='Random')
+    ax.set_xlabel('False Positive Rate', fontsize=12, fontweight='medium'); ax.set_ylabel('True Positive Rate', fontsize=12, fontweight='medium'); ax.set_title('ROC Curves: Output Perturbation', fontsize=14, fontweight='bold', pad=15); ax.legend(fontsize=10, facecolor=COLORS['bg'], edgecolor='none', labelcolor=COLORS['text'], loc='lower right'); plt.tight_layout()
     return fig
 
 def fig_tpr_at_low_fpr():
-    fig, axes = plt.subplots(1, 2, figsize=(16, 6.5)); apply_academic_style(fig, axes); labels_all, tpr5_all, tpr1_all, clrs_all, hatches_all = [], [], [], [], []
-    ls_h_list = [HATCH_BASELINE] + HATCH_LS
-    ls_c_list = [CHART_C['baseline']] + CHART_C['ls_colors']
+    fig, axes = plt.subplots(1, 2, figsize=(16, 6.5)); apply_light_style(fig, axes); labels_all, tpr5_all, tpr1_all, colors_all = [], [], [], []; ls_c = [COLORS['baseline']] + COLORS['ls_colors']
+    for i, (k, l) in enumerate(zip(LS_KEYS, LS_LABELS_PLOT)): labels_all.append(l); tpr5_all.append(gm(k, 'tpr_at_5fpr')); tpr1_all.append(gm(k, 'tpr_at_1fpr')); colors_all.append(ls_c[i])
+    for i, (k, l) in enumerate(zip(OP_KEYS, OP_LABELS_PLOT)): labels_all.append(l); tpr5_all.append(gm(k, 'tpr_at_5fpr')); tpr1_all.append(gm(k, 'tpr_at_1fpr')); colors_all.append(COLORS['op_colors'][i])
+    x = range(len(labels_all)); ax = axes[0]; bars = ax.bar(x, tpr5_all, color=colors_all, width=0.65, edgecolor='none', zorder=3)
+    for b, v in zip(bars, tpr5_all): ax.text(b.get_x()+b.get_width()/2, v+0.005, f'{v:.3f}', ha='center', fontsize=9, fontweight='semibold', color=COLORS['text'])
+    ax.set_ylabel('TPR @ 5% FPR', fontsize=12, fontweight='medium'); ax.set_title('Attack Power at 5% FPR', fontsize=14, fontweight='bold', pad=15); ax.set_xticks(x); ax.set_xticklabels(labels_all, rotation=35, ha='right', fontsize=11); ax.axhline(0.05, color=COLORS['warning'], ls='--', lw=1.5, alpha=0.7, label='Random (0.05)'); ax.legend(facecolor=COLORS['bg'], edgecolor='none', labelcolor=COLORS['text'], fontsize=10)
     
-    for i, (k, l) in enumerate(zip(LS_KEYS, LS_LABELS_PLOT)): 
-        labels_all.append(l); tpr5_all.append(gm(k, 'tpr_at_5fpr')); tpr1_all.append(gm(k, 'tpr_at_1fpr'))
-        clrs_all.append(ls_c_list[i]); hatches_all.append(ls_h_list[i])
-    for i, (k, l) in enumerate(zip(OP_KEYS, OP_LABELS_PLOT)): 
-        labels_all.append(l); tpr5_all.append(gm(k, 'tpr_at_5fpr')); tpr1_all.append(gm(k, 'tpr_at_1fpr'))
-        clrs_all.append(CHART_C['op_colors'][i]); hatches_all.append(HATCH_OP[i])
-        
-    x = range(len(labels_all)); ax = axes[0]; 
-    bars = ax.bar(x, tpr5_all, color=clrs_all, width=0.65, edgecolor='black', linewidth=1.5, zorder=3)
-    for bar, h in zip(bars, hatches_all):
-        if h: bar.set_hatch(h)
-    for b, v in zip(bars, tpr5_all): ax.text(b.get_x()+b.get_width()/2, v+0.005, f'{v:.3f}', ha='center', fontsize=9, fontweight='semibold', color='black')
-    ax.set_ylabel('TPR @ 5% FPR', fontsize=12, fontweight='medium'); ax.set_title('Attack Power at 5% FPR', fontsize=14, fontweight='bold', pad=15); ax.set_xticks(x); ax.set_xticklabels(labels_all, rotation=35, ha='right', fontsize=11); ax.axhline(0.05, color='gray', ls='--', lw=2, label='Random (0.05)'); ax.legend(facecolor='white', edgecolor='black', labelcolor='black', fontsize=10)
-    
-    ax = axes[1]; 
-    bars = ax.bar(x, tpr1_all, color=clrs_all, width=0.65, edgecolor='black', linewidth=1.5, zorder=3)
-    for bar, h in zip(bars, hatches_all):
-        if h: bar.set_hatch(h)
-    for b, v in zip(bars, tpr1_all): ax.text(b.get_x()+b.get_width()/2, v+0.003, f'{v:.3f}', ha='center', fontsize=9, fontweight='semibold', color='black')
-    ax.set_ylabel('TPR @ 1% FPR', fontsize=12, fontweight='medium'); ax.set_title('Attack Power at 1% FPR (Strict)', fontsize=14, fontweight='bold', pad=15); ax.set_xticks(x); ax.set_xticklabels(labels_all, rotation=35, ha='right', fontsize=11); ax.axhline(0.01, color='gray', ls='--', lw=2, label='Random (0.01)'); ax.legend(facecolor='white', edgecolor='black', labelcolor='black', fontsize=10); plt.tight_layout()
+    ax = axes[1]; bars = ax.bar(x, tpr1_all, color=colors_all, width=0.65, edgecolor='none', zorder=3)
+    for b, v in zip(bars, tpr1_all): ax.text(b.get_x()+b.get_width()/2, v+0.003, f'{v:.3f}', ha='center', fontsize=9, fontweight='semibold', color=COLORS['text'])
+    ax.set_ylabel('TPR @ 1% FPR', fontsize=12, fontweight='medium'); ax.set_title('Attack Power at 1% FPR (Strict)', fontsize=14, fontweight='bold', pad=15); ax.set_xticks(x); ax.set_xticklabels(labels_all, rotation=35, ha='right', fontsize=11); ax.axhline(0.01, color=COLORS['warning'], ls='--', lw=1.5, alpha=0.7, label='Random (0.01)'); ax.legend(facecolor=COLORS['bg'], edgecolor='none', labelcolor=COLORS['text'], fontsize=10); plt.tight_layout()
     return fig
 
 def fig_loss_gap_waterfall():
-    fig, ax = plt.subplots(figsize=(14, 6)); apply_academic_style(fig, ax); names, gaps, clrs, hatches = [], [], [], []
-    ls_h_list = [HATCH_BASELINE] + HATCH_LS
-    ls_c_list = [CHART_C['baseline']] + CHART_C['ls_colors']
-    for i, (k, l) in enumerate(zip(LS_KEYS, LS_LABELS_PLOT)): 
-        names.append(l); gaps.append(gm(k, 'loss_gap'))
-        clrs.append(ls_c_list[i]); hatches.append(ls_h_list[i])
-    for i, (k, l) in enumerate(zip(OP_KEYS, OP_LABELS_PLOT)): 
-        names.append(l); gaps.append(gm(k, 'loss_gap'))
-        clrs.append(CHART_C['op_colors'][i]); hatches.append(HATCH_OP[i])
-    
-    bars = ax.bar(range(len(names)), gaps, color=clrs, width=0.65, edgecolor='black', linewidth=1.5, zorder=3)
-    for bar, h in zip(bars, hatches):
-        if h: bar.set_hatch(h)
-    for b, v in zip(bars, gaps): ax.text(b.get_x()+b.get_width()/2, v+0.0005, f'{v:.4f}', ha='center', fontsize=10, fontweight='semibold', color='black')
-    ax.set_ylabel('Loss Gap', fontsize=12, fontweight='medium'); ax.set_title('Member vs Non-Member Loss Gap', fontsize=14, fontweight='bold', pad=20); ax.set_xticks(range(len(names))); ax.set_xticklabels(names, rotation=30, ha='right', fontsize=11); ax.annotate('Smaller gap = Better Privacy', xy=(8, gaps[0]*0.4), fontsize=11, color='black', fontstyle='italic', ha='center', backgroundcolor='white', bbox=dict(boxstyle='round,pad=0.4', facecolor='white', edgecolor='black', alpha=1.0)); plt.tight_layout()
+    fig, ax = plt.subplots(figsize=(14, 6)); apply_light_style(fig, ax); names, gaps, clrs = [], [], []; ls_c = [COLORS['baseline']] + COLORS['ls_colors']
+    for i, (k, l) in enumerate(zip(LS_KEYS, LS_LABELS_PLOT)): names.append(l); gaps.append(gm(k, 'loss_gap')); clrs.append(ls_c[i])
+    for i, (k, l) in enumerate(zip(OP_KEYS, OP_LABELS_PLOT)): names.append(l); gaps.append(gm(k, 'loss_gap')); clrs.append(COLORS['op_colors'][i])
+    bars = ax.bar(range(len(names)), gaps, color=clrs, width=0.65, edgecolor='none', zorder=3)
+    for b, v in zip(bars, gaps): ax.text(b.get_x()+b.get_width()/2, v+0.0005, f'{v:.4f}', ha='center', fontsize=10, fontweight='semibold', color=COLORS['text'])
+    ax.set_ylabel('Loss Gap', fontsize=12, fontweight='medium'); ax.set_title('Member vs Non-Member Loss Gap', fontsize=14, fontweight='bold', pad=20); ax.set_xticks(range(len(names))); ax.set_xticklabels(names, rotation=30, ha='right', fontsize=11); ax.annotate('Smaller gap = Better Privacy', xy=(8, gaps[0]*0.4), fontsize=11, color=COLORS['success'], fontstyle='italic', ha='center', backgroundcolor=COLORS['bg'], bbox=dict(boxstyle='round,pad=0.4', facecolor=COLORS['panel'], edgecolor=COLORS['success'], alpha=0.8)); plt.tight_layout()
     return fig
 
+# 🌟 效用页面柱状图
 def fig_acc_bar():
-    names, vals, clrs, hatches = [], [], [], []
-    ls_h_list = [HATCH_BASELINE] + HATCH_LS
-    ls_c_list = [CHART_C['baseline']] + CHART_C['ls_colors']
+    names, vals, clrs = [], [], []; ls_c = [COLORS['baseline']] + COLORS['ls_colors']
     for i, (k, l) in enumerate(zip(LS_KEYS, LS_LABELS_PLOT)):
-        if k in utility_results: 
-            names.append(l); vals.append(utility_results[k]['accuracy']*100)
-            clrs.append(ls_c_list[i]); hatches.append(ls_h_list[i])
+        if k in utility_results: names.append(l); vals.append(utility_results[k]['accuracy']*100); clrs.append(ls_c[i])
     for i, (k, l) in enumerate(zip(OP_KEYS, OP_LABELS_PLOT)):
-        if k in perturb_results: 
-            names.append(l); vals.append(bl_acc)
-            clrs.append(CHART_C['op_colors'][i]); hatches.append(HATCH_OP[i])
-        
-    fig, ax = plt.subplots(figsize=(12, 7)); apply_academic_style(fig, ax)
-    bars = ax.bar(range(len(names)), vals, color=clrs, width=0.65, edgecolor='black', linewidth=1.5, zorder=3)
-    for bar, h in zip(bars, hatches):
-        if h: bar.set_hatch(h)
-    for b, v in zip(bars, vals): ax.text(b.get_x()+b.get_width()/2, v+1, f'{v:.1f}%', ha='center', fontsize=11, fontweight='bold', color='black')
+        if k in perturb_results: names.append(l); vals.append(bl_acc); clrs.append(COLORS['op_colors'][i])
+    fig, ax = plt.subplots(figsize=(12, 7)); apply_light_style(fig, ax); bars = ax.bar(range(len(names)), vals, color=clrs, width=0.65, edgecolor='none', zorder=3)
+    for b, v in zip(bars, vals): ax.text(b.get_x()+b.get_width()/2, v+1, f'{v:.1f}%', ha='center', fontsize=11, fontweight='bold', color=COLORS['text'])
     ax.set_ylabel('Test Accuracy (%)', fontsize=12, fontweight='medium'); ax.set_title('Model Utility: Test Accuracy', fontsize=15, fontweight='bold', pad=20)
     ax.set_ylim(0, 105); ax.set_xticks(range(len(names))); ax.set_xticklabels(names, rotation=35, ha='right', fontsize=12); plt.tight_layout()
     return fig
 
+# 🌟 修复：散点图增加透明度防遮挡，并且图例移至左下角空白处
 def fig_tradeoff():
-    fig, ax = plt.subplots(figsize=(12, 7)); apply_academic_style(fig, ax); 
-    markers_ls = ['o', 's', 'p', '*', 'h']
+    fig, ax = plt.subplots(figsize=(12, 7)); apply_light_style(fig, ax); markers_ls = ['o', 's', 's', 's', 's']; ls_c = [COLORS['baseline']] + COLORS['ls_colors']
     for i, (k, l) in enumerate(zip(LS_KEYS, LS_LABELS_PLOT)):
-        if k in mia_results and k in utility_results: 
-            ax.scatter(utility_results[k]['accuracy']*100, mia_results[k]['auc'], label=l, marker=markers_ls[i], color='white', s=250, edgecolors='black', lw=2.0, zorder=5)
-    op_markers = ['^', 'D', 'v', 'P', 'X', '>']
+        if k in mia_results and k in utility_results: ax.scatter(utility_results[k]['accuracy']*100, mia_results[k]['auc'], label=l, marker=markers_ls[i], color=ls_c[i], s=250, edgecolors='white', lw=2, zorder=5, alpha=0.9)
+    op_markers = ['^', 'D', 'v', 'P', 'X', 'h']
     for i, (k, l) in enumerate(zip(OP_KEYS, OP_LABELS_PLOT)):
-        if k in perturb_results: 
-            ax.scatter(bl_acc, perturb_results[k]['auc'], label=l, marker=op_markers[i], color='#AAAAAA', s=200, edgecolors='black', lw=1.5, zorder=6)
+        # 增加透明度 alpha=0.75 防止叠放时遮盖
+        if k in perturb_results: ax.scatter(bl_acc, perturb_results[k]['auc'], label=l, marker=op_markers[i], color=COLORS['op_colors'][i], s=200, edgecolors='white', lw=1.5, zorder=6, alpha=0.75)
     
-    ax.axhline(0.5, color='black', ls='--', lw=1.5, alpha=0.6, label='Random (AUC=0.5)')
-    ax.annotate('IDEAL ZONE\nHigh Utility, Low Risk', xy=(85, 0.51), fontsize=11, fontweight='bold', color='black', ha='center', bbox=dict(boxstyle='round,pad=0.5', fc='white', ec='black'))
-    ax.annotate('HIGH RISK ZONE\nLow Utility, High Risk', xy=(62, 0.61), fontsize=11, fontweight='bold', color='black', ha='center', bbox=dict(boxstyle='round,pad=0.5', fc='white', ec='black'))
+    ax.axhline(0.5, color=COLORS['text_dim'], ls='--', alpha=0.6, label='Random (AUC=0.5)')
+    ax.annotate('IDEAL ZONE\nHigh Utility, Low Risk', xy=(85, 0.51), fontsize=11, fontweight='bold', color=COLORS['success'], alpha=0.7, ha='center', backgroundcolor=COLORS['bg'])
+    ax.annotate('HIGH RISK ZONE\nLow Utility, High Risk', xy=(62, 0.61), fontsize=11, fontweight='bold', color=COLORS['danger'], alpha=0.7, ha='center', backgroundcolor=COLORS['bg'])
     ax.set_xlabel('Model Utility (Accuracy %)', fontsize=12, fontweight='medium'); ax.set_ylabel('Privacy Risk (MIA AUC)', fontsize=12, fontweight='medium')
     ax.set_title('Privacy-Utility Trade-off Analysis', fontsize=15, fontweight='bold', pad=20)
-    ax.legend(fontsize=11, loc='lower left', ncol=2, facecolor='white', edgecolor='black', labelcolor='black'); plt.tight_layout()
+    # 图例放到绝对安全的左下角空白区域
+    ax.legend(fontsize=11, loc='lower left', ncol=2, facecolor=COLORS['bg'], edgecolor='none'); plt.tight_layout()
     return fig
 
+# 🌟 修复：趋势图图例移位，避免遮挡数据线和阴影
 def fig_auc_trend():
-    fig, axes = plt.subplots(1, 2, figsize=(16, 6.5)); apply_academic_style(fig, axes); ax = axes[0]; eps_vals = [0.0, 0.02, 0.05, 0.1, 0.2]; auc_vals = [gm(k, 'auc') for k in LS_KEYS]; acc_vals = [gu(k) for k in LS_KEYS]
-    ax2 = ax.twinx(); 
-    line1 = ax.plot(eps_vals, auc_vals, marker='o', ls='-', color='black', lw=3, ms=9, label='MIA AUC (Risk)', zorder=5); 
-    line2 = ax2.plot(eps_vals, acc_vals, marker='s', ls='--', color='black', lw=3, ms=9, label='Utility % (right)', zorder=5); 
-    ax.axhline(0.5, color='gray', ls=':')
-    ax.fill_between(eps_vals, auc_vals, 0.5, alpha=0.2, color='gray', hatch='//')
-    ax.set_xlabel('Label Smoothing ε', fontsize=12, fontweight='medium'); ax.set_ylabel('MIA AUC', fontsize=12, fontweight='medium', color='black'); ax2.set_ylabel('Utility (%)', fontsize=12, fontweight='medium', color='black'); ax.set_title('Label Smoothing Trends', fontsize=14, fontweight='bold', pad=15); ax.tick_params(axis='y', labelcolor='black'); ax2.tick_params(axis='y', labelcolor='black'); ax2.spines['right'].set_color('black'); ax2.spines['left'].set_color('black'); lines = line1 + line2; labels = [l.get_label() for l in lines]
-    ax.legend(lines, labels, fontsize=10, facecolor='white', edgecolor='black', loc='lower right')
+    fig, axes = plt.subplots(1, 2, figsize=(16, 6.5)); apply_light_style(fig, axes); ax = axes[0]; eps_vals = [0.0, 0.02, 0.05, 0.1, 0.2]; auc_vals = [gm(k, 'auc') for k in LS_KEYS]; acc_vals = [gu(k) for k in LS_KEYS]
+    ax2 = ax.twinx(); line1 = ax.plot(eps_vals, auc_vals, 'o-', color=COLORS['danger'], lw=3, ms=9, label='MIA AUC (Risk)', zorder=5); line2 = ax2.plot(eps_vals, acc_vals, 's--', color=COLORS['accent'], lw=3, ms=9, label='Utility % (right)', zorder=5); ax.axhline(0.5, color=COLORS['text_dim'], ls=':', alpha=0.5)
+    ax.fill_between(eps_vals, auc_vals, 0.5, alpha=0.08, color=COLORS['danger'])
+    ax.set_xlabel('Label Smoothing ε', fontsize=12, fontweight='medium'); ax.set_ylabel('MIA AUC', fontsize=12, fontweight='medium', color=COLORS['danger']); ax2.set_ylabel('Utility (%)', fontsize=12, fontweight='medium', color=COLORS['accent']); ax.set_title('Label Smoothing Trends', fontsize=14, fontweight='bold', pad=15); ax.tick_params(axis='y', labelcolor=COLORS['danger']); ax2.tick_params(axis='y', labelcolor=COLORS['accent']); ax2.spines['right'].set_color(COLORS['accent']); ax2.spines['left'].set_color(COLORS['danger']); lines = line1 + line2; labels = [l.get_label() for l in lines]
+    # 图例移至右下角安全区
+    ax.legend(lines, labels, fontsize=10, facecolor=COLORS['bg'], edgecolor='none', loc='lower right')
     
-    ax = axes[1]; sig_vals = OP_SIGMAS; auc_op = [gm(k, 'auc') for k in OP_KEYS]; 
-    ax.plot(sig_vals, auc_op, marker='^', ls='-', color='black', lw=3, ms=9, zorder=5, label='MIA AUC'); 
-    ax.axhline(bl_auc, color='black', ls='--', lw=2, label=f'Baseline ({bl_auc:.4f})'); 
-    ax.axhline(0.5, color='gray', ls=':', label='Random (0.5)'); 
-    ax.fill_between(sig_vals, auc_op, bl_auc, alpha=0.2, color='gray', hatch='\\\\', label='AUC Reduction')
-    ax2r = ax.twinx(); ax2r.axhline(bl_acc, color='black', ls='-', lw=2.5); ax2r.set_ylabel(f'Utility = {bl_acc:.1f}% (unchanged)', fontsize=12, fontweight='medium', color='black'); ax2r.set_ylim(0,100); ax2r.tick_params(axis='y', labelcolor='black'); ax2r.spines['right'].set_color('black')
+    ax = axes[1]; sig_vals = OP_SIGMAS; auc_op = [gm(k, 'auc') for k in OP_KEYS]; ax.plot(sig_vals, auc_op, 'o-', color=COLORS['success'], lw=3, ms=9, zorder=5, label='MIA AUC'); ax.axhline(bl_auc, color=COLORS['danger'], ls='--', lw=2, alpha=0.6, label=f'Baseline ({bl_auc:.4f})'); ax.axhline(0.5, color=COLORS['text_dim'], ls=':', alpha=0.5, label='Random (0.5)'); ax.fill_between(sig_vals, auc_op, bl_auc, alpha=0.2, color=COLORS['success'], label='AUC Reduction')
+    ax2r = ax.twinx(); ax2r.axhline(bl_acc, color=COLORS['success'], ls='-', lw=2.5, alpha=0.8); ax2r.set_ylabel(f'Utility = {bl_acc:.1f}% (unchanged)', fontsize=12, fontweight='medium', color=COLORS['success']); ax2r.set_ylim(0,100); ax2r.tick_params(axis='y', labelcolor=COLORS['success']); ax2r.spines['right'].set_color(COLORS['success'])
     ax.set_xlabel('Perturbation σ', fontsize=12, fontweight='medium'); ax.set_ylabel('MIA AUC', fontsize=12, fontweight='medium'); ax.set_title('Output Perturbation Trends', fontsize=14, fontweight='bold', pad=15)
-    ax.legend(fontsize=10, facecolor='white', edgecolor='black', loc='lower left'); plt.tight_layout()
+    # 图例移至左下角安全区
+    ax.legend(fontsize=10, facecolor=COLORS['bg'], edgecolor='none', loc='lower left'); plt.tight_layout()
     return fig
 
 # ================================================================
@@ -541,7 +408,6 @@ ATK_CHOICES = (
     [f"标签平滑 (ε={e})" for e in [0.02, 0.05, 0.1, 0.2]] +
     [f"输出扰动 (σ={s})" for s in OP_SIGMAS]
 )
-
 ATK_MAP = {"基线模型 (Baseline)": "baseline"}
 for e in [0.02, 0.05, 0.1, 0.2]: ATK_MAP[f"标签平滑 (ε={e})"] = f"smooth_eps_{e}"
 for s in OP_SIGMAS: ATK_MAP[f"输出扰动 (σ={s})"] = f"perturbation_{s}"
@@ -708,9 +574,9 @@ footer { display: none !important; }
 """
 
 # ================================================================
-# UI 布局构建 (完全不碰原版Blocks构建)
+# UI 布局构建
 # ================================================================
-with gr.Blocks(title="MIA攻防研究") as demo:
+with gr.Blocks(title="MIA攻防研究", theme=gr.themes.Soft(), css=CSS) as demo:
     
     gr.HTML("""<div class="title-area">
         <h1>🎓 教育大模型中的成员推理攻击及其防御研究</h1>
@@ -863,7 +729,7 @@ with gr.Blocks(title="MIA攻防研究") as demo:
         gr.HTML('<div style="margin:40px 0 8px;"><span class="dim-label dim2">维度二</span><strong style="font-size:18px;color:#1D2939;">极限实战维度 — 证明“极低误报下的安全底线”</strong></div>')
         gr.Markdown(f"""\
 > **实战意义：** 现实中黑客只允许极低的误报（如 1%）。在 Baseline 中，1% 误报率下黑客依然能精准窃取 **{gm('baseline','tpr_at_1fpr')*100:.1f}%** 的真实隐私（红柱子极高）。
-> 开启 OP(σ=0.03) 防防御后，该成功率被死死压制到了 **{gm('perturbation_0.03','tpr_at_1fpr')*100:.1f}%**。这证明在最极端的实战条件下，防线依然坚固。
+> 开启 OP(σ=0.03) 防御后，该成功率被死死压制到了 **{gm('perturbation_0.03','tpr_at_1fpr')*100:.1f}%**。这证明在最极端的实战条件下，防线依然坚固。
 """)
         gr.Plot(value=fig_tpr_at_low_fpr())
 
@@ -873,6 +739,7 @@ with gr.Blocks(title="MIA攻防研究") as demo:
 > **LS 的防御本质：** 随着 ε 增大，两座山峰趋于完美重合，均值差距缩小到了 {gm('smooth_eps_0.2','loss_gap'):.4f}。这是从物理上抹除了模型记忆。
 > **OP 的防御本质：** 均值差距未变，但高斯噪声导致分布变得极其扁平宽阔，红蓝区域被完全搅混，蒙蔽了攻击者的双眼。
 """)
+        # 🌟 调用专门添加的三联横向对比直方图
         gr.Plot(value=fig_d3_dist_compare()) 
         gr.Plot(value=fig_loss_gap_waterfall())
         
@@ -989,5 +856,4 @@ with gr.Blocks(title="MIA攻防研究") as demo:
 
 """)
 
-# 强制开启公网穿透 (share=True) 和 终端���错显示 (debug=True)
-demo.launch(theme=gr.themes.Soft(), css=CSS, share=True, debug=True, inline=False)
+demo.launch(theme=gr.themes.Soft(), css=CSS)