""" Image Forensics for Scientific Figure Manipulation Detection Specialized for western blots, gel electrophoresis, microscopy images. """ import torch import torch.nn as nn import numpy as np from PIL import Image import cv2 class ForensicFilterBank(nn.Module): """Bank of forensic filters for detecting image manipulation in scientific figures.""" def __init__(self): super().__init__() # SRM (Spatial Rich Model) filters for noise residuals self.srm_filters = nn.Conv2d(1, 9, kernel_size=5, padding=2, bias=False) srm_kernels = torch.tensor([ [[0, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, -1, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 1, -1, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, -1, 1, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], [[0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, -1, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, -1, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 0]], [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 1, -2, 1, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], [[0, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, -2, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 0]], [[0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, -2, 0, 0], [0, 1, 0, 0, 0], [0, 0, 0, 0, 0]], [[0, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, -2, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 0, 0]] ], dtype=torch.float32).unsqueeze(1) self.srm_filters.weight = nn.Parameter(srm_kernels) for param in self.srm_filters.parameters(): param.requires_grad = False # Bayar filter for boundary artifacts self.bayar_filter = nn.Conv2d(1, 3, kernel_size=5, padding=2, bias=False) bayar_kernel = torch.zeros(3, 1, 5, 5) bayar_kernel[0, 0, 2, 2] = -1 bayar_kernel[0, 0, 2, 1] = 1 bayar_kernel[1, 0, 2, 2] = -1 bayar_kernel[1, 0, 1, 2] = 1 bayar_kernel[2, 0, 2, 2] = -1 bayar_kernel[2, 0, 2, 3] = 1 self.bayar_filter.weight = nn.Parameter(bayar_kernel) for param in self.bayar_filter.parameters(): param.requires_grad = False # Error Level Analysis (ELA) simulation self.ela_conv = nn.Sequential( nn.Conv2d(3, 16, 3, padding=1), nn.ReLU(), nn.Conv2d(16, 16, 3, padding=1), nn.ReLU() ) def forward(self, image): gray = 0.299 * image[:, 0:1] + 0.587 * image[:, 1:2] + 0.114 * image[:, 2:3] srm_out = self.srm_filters(gray) bayar_out = self.bayar_filter(gray) ela_out = self.ela_conv(image) target_size = image.shape[2:] srm_out = torch.nn.functional.interpolate(srm_out, size=target_size, mode='bilinear') bayar_out = torch.nn.functional.interpolate(bayar_out, size=target_size, mode='bilinear') forensic = torch.cat([srm_out, bayar_out, ela_out], dim=1) return forensic class WesternBlotAnalyzer: """Specialized analyzer for western blot image manipulation detection.""" @staticmethod def detect_band_duplication(image, threshold=0.95): if isinstance(image, str): img = cv2.imread(image, cv2.IMREAD_GRAYSCALE) else: img = np.array(image.convert('L')) _, binary = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU) num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(binary, connectivity=8) bands = [] for i in range(1, num_labels): x, y, w, h, area = stats[i] if area > 50: band = img[y:y+h, x:x+w] bands.append((i, band, (x, y, w, h))) duplicates = [] for i in range(len(bands)): for j in range(i+1, len(bands)): band1 = bands[i][1] band2 = bands[j][1] h = max(band1.shape[0], band2.shape[0]) w = max(band1.shape[1], band2.shape[1]) b1 = cv2.resize(band1, (w, h)) b2 = cv2.resize(band2, (w, h)) similarity = np.corrcoef(b1.flatten(), b2.flatten())[0, 1] if similarity > threshold: duplicates.append((bands[i][0], bands[j][0], similarity)) return duplicates @staticmethod def detect_splicing_artifacts(image): if isinstance(image, str): img = cv2.imread(image) else: img = np.array(image) gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) if len(img.shape) == 3 else img edges = cv2.Canny(gray, 50, 150) lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=100, minLineLength=100, maxLineGap=10) artifacts = [] if lines is not None: for line in lines: x1, y1, x2, y2 = line[0] if abs(x2 - x1) > abs(y2 - y1): y = (y1 + y2) // 2 region_above = gray[max(0, y-5):y, min(x1,x2):max(x1,x2)] region_below = gray[y:min(gray.shape[0], y+5), min(x1,x2):max(x1,x2)] if region_above.size > 0 and region_below.size > 0: noise_above = np.std(region_above.astype(float)) noise_below = np.std(region_below.astype(float)) if abs(noise_above - noise_below) > 5: artifacts.append({'line': (x1, y1, x2, y2), 'noise_diff': abs(noise_above - noise_below)}) return artifacts @staticmethod def compute_manipulation_score(image): score = 0.0 reasons = [] duplicates = WesternBlotAnalyzer.detect_band_duplication(image) if duplicates: score += min(0.4, len(duplicates) * 0.1) reasons.append(f"Band duplication detected: {len(duplicates)} pairs") artifacts = WesternBlotAnalyzer.detect_splicing_artifacts(image) if artifacts: score += min(0.3, len(artifacts) * 0.05) reasons.append(f"Splicing artifacts: {len(artifacts)} boundaries") if isinstance(image, str): img = cv2.imread(image, cv2.IMREAD_GRAYSCALE) else: img = np.array(image.convert('L')) h, w = img.shape sub_size = min(h, w) // 4 if sub_size > 20: hashes = [] for i in range(0, h - sub_size, sub_size // 2): for j in range(0, w - sub_size, sub_size // 2): sub = img[i:i+sub_size, j:j+sub_size] hashes.append((i, j, np.mean(sub))) for i in range(len(hashes)): for j in range(i+1, len(hashes)): if abs(hashes[i][2] - hashes[j][2]) < 1.0: score += 0.01 score = min(score, 1.0) return score, reasons class ScientificImageForensics(nn.Module): """Complete forensics pipeline for scientific images.""" def __init__(self): super().__init__() self.filter_bank = ForensicFilterBank() self.manipulation_cnn = nn.Sequential( nn.Conv2d(28, 32, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2), nn.Conv2d(32, 64, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2), nn.Conv2d(64, 128, 3, padding=1), nn.ReLU(), nn.AdaptiveAvgPool2d((4, 4)), nn.Flatten(), nn.Linear(128 * 4 * 4, 256), nn.ReLU(), nn.Dropout(0.3), nn.Linear(256, 1), nn.Sigmoid() ) def forward(self, image): forensic_features = self.filter_bank(image) manipulation_prob = self.manipulation_cnn(forensic_features) return manipulation_prob, forensic_features