Initial: AI Image Detector with Organika/sdxl-detector baseline

README.md

@@ -1,13 +1,29 @@
 ---
-title: Ai Image Detector
-emoji: 😻
-colorFrom: purple
-colorTo: yellow
+title: AI Image Detector
+emoji: 🛡️
+colorFrom: blue
+colorTo: indigo
 sdk: gradio
-sdk_version: 6.14.0
-python_version: '3.13'
+sdk_version: 4.44.0
 app_file: app.py
-pinned: false
+pinned: true
+short_description: Open-baseline AI-generated image detection by Scam.AI
+license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# AI Image Detector — Scam.AI 🛡️
+
+Upload any image and an open-source baseline (`Organika/sdxl-detector`) will tell you whether it thinks the image is AI-generated.
+
+**This is a baseline demo**, not a production system. Open detectors are known to fail on:
+- Newer generators (GPT-Image-2, FLUX, Imagen 3)
+- Post-processed images (super-resolution, JPEG re-compression)
+- Out-of-domain content (documents, medical, surveillance)
+
+We published a [comprehensive benchmark](https://huggingface.co/datasets/Scam-AI/gpt-image-2) showing >30 AUC point gaps between academic and real-world performance.
+
+For production-grade detection (calibrated, adversarial-robust, domain-specific) — visit **[scam.ai](https://www.scam.ai)**.
+
+## Built by Scam.AI
+
+Detection systems for AI-driven fraud. See our [research](https://www.scam.ai/en/research) and [open datasets](https://huggingface.co/Scam-AI).

app.py

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+"""AI Image Detector (Open Baseline) — Scam.AI
+
+Wraps the open-source Organika/sdxl-detector model in a Gradio interface.
+The output explicitly positions this as a community baseline, with a CTA
+toward Scam.AI's production-grade detection systems.
+"""
+
+import gradio as gr
+from transformers import pipeline
+
+# Open-source baseline (192K downloads, Swin-based, ~110MB)
+BASELINE_MODEL = "Organika/sdxl-detector"
+
+print(f"Loading baseline model: {BASELINE_MODEL}")
+clf = pipeline("image-classification", model=BASELINE_MODEL)
+print("Model loaded.")
+
+
+def detect(img):
+    if img is None:
+        return None, "Upload an image to analyze."
+
+    results = clf(img)
+    # results: list of dicts {label, score}
+    scores = {r["label"]: float(r["score"]) for r in results}
+
+    # Find the AI-generated probability
+    ai_keys = [k for k in scores if "artificial" in k.lower() or "ai" in k.lower() or "fake" in k.lower() or "generated" in k.lower()]
+    if ai_keys:
+        ai_prob = scores[ai_keys[0]]
+    else:
+        # fallback — highest score label is "ai" if model uses different naming
+        ai_prob = max(scores.values()) if list(scores.keys())[0].lower() not in ("human", "real") else 1 - max(scores.values())
+
+    if ai_prob > 0.85:
+        verdict = "🤖 **Likely AI-generated**"
+        explanation = (
+            f"This open-source baseline is **{ai_prob*100:.0f}% confident** "
+            f"the image is AI-generated."
+        )
+    elif ai_prob > 0.5:
+        verdict = "⚠️ **Possibly AI-generated**"
+        explanation = (
+            f"The baseline leans toward AI ({ai_prob*100:.0f}%) but with low "
+            f"confidence. In our experience this is exactly the regime where "
+            f"open models fail — they catch the obvious cases but miss the "
+            f"sophisticated ones."
+        )
+    else:
+        verdict = "📷 **Likely real photograph**"
+        explanation = (
+            f"Baseline confidence in 'real': {(1-ai_prob)*100:.0f}%. "
+            f"Note: open baselines have well-known false-negative blind spots "
+            f"on newer generators (GPT-Image-2, FLUX, etc)."
+        )
+
+    cta = (
+        "\n\n---\n\n"
+        "### 📌 About this baseline\n\n"
+        f"Model: [`{BASELINE_MODEL}`](https://huggingface.co/{BASELINE_MODEL}) "
+        "— a community Swin-Transformer trained for SDXL detection. "
+        "Useful as a sanity check but does **not** generalize well to:\n"
+        "- Newer generators (GPT-Image-2, FLUX.1, Imagen 3)\n"
+        "- Heavily post-processed images (super-resolution, JPEG re-compression)\n"
+        "- Domain shift (medical, document, surveillance)\n\n"
+        "Our team published [a comprehensive benchmark](https://huggingface.co/datasets/Scam-AI/gpt-image-2) "
+        "showing 30+ AUC-point gaps between in-distribution academic tests "
+        "and real-world performance.\n\n"
+        "**For production deployment** — APIs, on-premise inference, custom "
+        "domain fine-tuning — talk to us at **[scam.ai](https://www.scam.ai)**."
+    )
+
+    return scores, verdict + "\n\n" + explanation + cta
+
+
+with gr.Blocks(
+    title="AI Image Detector — Scam.AI",
+    theme=gr.themes.Soft(primary_hue="blue"),
+) as demo:
+    gr.Markdown(
+        "# 🛡️ AI Image Detector\n"
+        "*Drop an image to check whether an open-source baseline thinks it's "
+        "AI-generated.*\n\n"
+        "*Built by [Scam.AI](https://www.scam.ai) · Powered by "
+        f"[`{BASELINE_MODEL}`](https://huggingface.co/{BASELINE_MODEL})*"
+    )
+
+    with gr.Row():
+        with gr.Column():
+            inp = gr.Image(type="pil", label="Upload an image")
+            btn = gr.Button("Analyze", variant="primary", size="lg")
+            gr.Examples(
+                examples=[],
+                inputs=inp,
+            )
+        with gr.Column():
+            scores = gr.Label(label="Class scores")
+            verdict = gr.Markdown()
+
+    btn.click(detect, inputs=inp, outputs=[scores, verdict])
+    inp.change(detect, inputs=inp, outputs=[scores, verdict])
+
+    gr.Markdown(
+        "---\n"
+        "**About:** This Space uses a community-trained open baseline to "
+        "demonstrate the AI-image-detection task. Real production systems "
+        "(higher accuracy, calibrated probabilities, robust to adversarial "
+        "post-processing) are not open. Visit [scam.ai](https://www.scam.ai) "
+        "if you need detection for actual deployment."
+    )
+
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio>=4.0
+transformers>=4.40
+torch
+pillow