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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
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