\n This notebook serves as a comprehensive guide for the \n \"Recod.ai/LUC - Scientific Image Forgery Detection\" competition.\n The primary goal is to develop a robust model capable of \n detecting and segmenting copy-move forgeries \n in biomedical research images at the pixel level.\n
\n\n\n The goal is not just to build a model, but to create an educational resource \n that explains the why and how β making advanced image forensics \n methods understandable for all participants.\n
\n\n The integrity of scientific imagery is fundamental to credible research. \n Images in publications should represent genuine data β yet manipulation persists. \n A common and damaging practice is copy-move forgery, \n where regions of an image are duplicated to fabricate or reinforce results.\n
\n\n\n The consequences are far-reaching:\n
\n\n This competition uses a realistic benchmark dataset derived from \n hundreds of confirmed forgeries across over 2,000 retracted scientific papers β \n making it one of the most detailed datasets in the field of image forensics.\n
\n\n Retractions are not abstract β they represent a growing crisis in scientific publishing. \n The following table summarizes insights from studies on retracted research, \n emphasizing the urgency of tools like this competition aims to build.\n
\n\n| Aspect | \nFindings from Recent Research | \nRelevance to this Competition | \n
|---|---|---|
| Retraction Scale & Impact | \nThousands of retracted papers have been analyzed using ML. One study examined 764 retracted AI papers. | \nProvides a rich foundation of real-world fraudulent cases for training detection models. | \n
| Persistence of Flawed Work | \n96% of citations to a retracted clinical trial failed to note its retraction. | \nHighlights the importance of detecting manipulation before publication. | \n
| Common Reasons for Retraction | \nIn AI and biomedical fields, causes include data falsification and image manipulation. | \nCopy-move forgery detection targets one of the root causes directly. | \n
| AIβs Role in the Problem | \nAI systems sometimes reuse content from retracted papers without warning. | \nShows the need for reliable, ethical AI systems that safeguard research integrity. | \n
\n These studies confirm that the issue this competition tackles is real, urgent, and impactful. \n By contributing here, you help develop technologies that can \n protect the integrity of scientific discovery.\n
\nComprehensive review of image segmentation techniques, from traditional to deep learning approaches.
\n\nBefore the rise of deep learning, image segmentation relied on mathematical models and low-level features like intensity and texture. The following table summarizes their key characteristics, which are useful for understanding the fundamentals of image analysis:
\n\n| Method Type | \nKey Idea | \nStrengths | \nWeaknesses in Medical/Forgery Context | \n
|---|---|---|---|
| Thresholding | \nConverts a grayscale image to binary based on a pixel value threshold. | \nComputationally efficient, simple to implement. | \nStruggles with intensity variations, noise, and complex structures. | \n
| Edge-Based | \nDetects boundaries between regions by finding sharp intensity changes. | \nGood at identifying clear contours. | \nSensitive to noise; struggles with discontinuous or blurred edges common in medical images. | \n
| Region-Based | \nGroups pixels with similar properties into contiguous regions. | \nCan produce coherent regions. | \nOften requires manual seed points; performance depends on initial choices. | \n
| Clustering | \nGroups pixels into clusters based on feature similarity (e.g., K-Means). | \nUnsupervised; no need for labeled data. | \nStruggles with complex shapes and high variability of forged regions. | \n
Deep learning, particularly Convolutional Neural Networks (CNNs), has dramatically improved segmentation performance by automatically learning hierarchical features from data.
\n\nFully Convolutional Networks (FCNs) were a pivotal step, demonstrating that networks could perform dense pixel-wise prediction, which is the foundation of modern segmentation models.
\n\nChoosing the right optimizer is crucial for efficiently training these deep models. While Stochastic Gradient Descent (SGD) is a classic choice, adaptive optimizers are often preferred:
\n\n\nBased on current research and benchmarking studies, both U-Net and Mask R-CNN are top-tier candidates for segmentation in this competition. \nU-Net often achieves higher pixel-level accuracy, while Mask R-CNN offers the flexibility of instance-level detection. \nThe table below highlights their main differences to help you make an informed choice.\n
\n\n| Feature | \nU-Net (Recommended) | \nMask R-CNN | \n
|---|---|---|
| Architecture Type | \nSemantic Segmentation | \nInstance Segmentation | \n
| Core Strength | \nPixel-level classification with highly precise boundaries | \nDistinguishes between individual object instances | \n
| Ideal For | \nThis competition's goal β detecting forged or manipulated regions | \nScenes with multiple, distinct objects | \n
| Key Advantage | \nSuperior Dice/IoU scores in biomedical and forgery detection tasks | \nProvides both bounding boxes and segmentation masks | \n
| Performance (Example) | \nDice: 0.96, IoU: 0.97 (Panoramic Radiographs) | \n Dice: 0.87, IoU: 0.74 (Panoramic Radiographs) | \n
| Computational Cost | \nGenerally lower β lightweight and fast | \nHigher β more complex due to multi-stage detection | \n
\nFor the highest segmentation accuracy, start with a standard U-Net implementation. Once itβs working reliably, you can enhance it with modern variants such as U-Net++, Attention U-Net, or ResUNet for improved boundary precision and generalization.\n
\n\nThis foundational approach constructs the classic encoderβdecoder structure with skip connections that preserve fine spatial details:
\n\n\n","metadata":{}},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow.keras.layers import *\nfrom tensorflow.keras.models import Model\n\ndef build_unet(input_size=(128, 128, 1)):\n inputs = Input(input_size)\n \n # Encoder (Contracting Path)\n c1 = Conv2D(64, (3, 3), activation='relu', padding='same')(inputs)\n c1 = Conv2D(64, (3, 3), activation='relu', padding='same')(c1)\n p1 = MaxPooling2D((2, 2))(c1)\n\n c2 = Conv2D(128, (3, 3), activation='relu', padding='same')(p1)\n c2 = Conv2D(128, (3, 3), activation='relu', padding='same')(c2)\n p2 = MaxPooling2D((2, 2))(c2)\n\n # Bottleneck\n b = Conv2D(256, (3, 3), activation='relu', padding='same')(p2)\n b = Conv2D(256, (3, 3), activation='relu', padding='same')(b)\n\n # Decoder (Expanding Path)\n u1 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(b)\n u1 = concatenate([u1, c2])\n u1 = Conv2D(128, (3, 3), activation='relu', padding='same')(u1)\n u1 = Conv2D(128, (3, 3), activation='relu', padding='same')(u1)\n\n u2 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(u1)\n u2 = concatenate([u2, c1])\n u2 = Conv2D(64, (3, 3), activation='relu', padding='same')(u2)\n u2 = Conv2D(64, (3, 3), activation='relu', padding='same')(u2)\n\n outputs = Conv2D(1, (1, 1), activation='sigmoid')(u2)\n \n model = Model(inputs=[inputs], outputs=[outputs])\n return model\n\n# Build and compile the model\nmodel = build_unet()\nmodel.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\nmodel.summary()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-25T12:48:03.313931Z","iopub.execute_input":"2025-10-25T12:48:03.314260Z","iopub.status.idle":"2025-10-25T12:48:21.992722Z","shell.execute_reply.started":"2025-10-25T12:48:03.314231Z","shell.execute_reply":"2025-10-25T12:48:21.991870Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"\n\n To push a U-Net baseline toward state-of-the-art performance on the forgery detection task, combine loss, pretraining and architectural improvements from recent literature. \n Below are practical, research-backed recommendations with short explanations and implementation tips.\n
\n\n\n Dice directly measures overlap between predicted and ground-truth masks and correlates with the competition F1-style metric. In practice, combining Dice with a pixel-wise loss (BCE) stabilizes training:\n
\n\n\n# PyTorch: simple combined Dice + BCE loss\nimport torch\nimport torch.nn.functional as F\n\ndef dice_loss(pred, target, eps=1e-6):\n # pred: logits or probabilities\n pred = torch.sigmoid(pred).view(-1)\n target = target.view(-1).float()\n intersection = (pred * target).sum()\n return 1 - (2. * intersection + eps) / (pred.sum() + target.sum() + eps)\n\ndef bce_dice_loss(logits, target, bce_weight=0.5):\n bce = F.binary_cross_entropy_with_logits(logits, target.float())\n d_loss = dice_loss(logits, target)\n return bce_weight * bce + (1 - bce_weight) * d_loss\n\n\n
\n Masked pretraining asks the network to reconstruct randomly masked image patches. This forces the encoder and decoder to learn strong context and texture priors without labels. Benefits:\n
\n\n Practical recipe: pretrain the U-Net to predict the original pixels for randomly masked patches (e.g., 25β50% of pixels masked). Use L1 or L2 reconstruction loss. Then fine-tune with the segmentation loss on labeled masks.\n
\n\n\n Replace or augment the vanilla U-Net building blocks with these modern ideas to improve receptive field, boundary accuracy, and parameter efficiency:\n
\n\n| Variant | \nCore Idea | \nWhy it helps | \n
|---|---|---|
| MS-UNet | \nMulti-scale feature fusion across encoder/decoder levels | \nCaptures both fine details and global context with fewer parameters | \n
| U-Tunnel-Net | \nImproved feature preservation via enhanced skip-path design and residual tunnels | \nBetter restoration of subtle textures and boundaries β ideal for small forged patches | \n
| Attention U-Net | \nGates that reweight encoder features before skip connections | \nFocuses model capacity on relevant regions, reducing false positives | \n
\n If you want, I can now generate:\n
train, validation, and test folders.ResNet34 or EfficientNet-B0 for faster convergence.\n Dice Loss with Binary Cross-Entropy for balanced learning.1e-4) and use a scheduler like ReduceLROnPlateau.TensorBoard or Weights & Biases for better visualization.\n π‘ Pro Tip: Always validate results on unseen data and monitor overfitting. Visual inspection is key in segmentation tasks β numbers alone don't tell the full story!
\n","metadata":{}},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}