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| pipeline_tag: image-to-image |
| tags: |
| - denoiser |
| - residual |
| --- |
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| A 2 Million Parameter Depoising Model and a Poison Detection Model |
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| Code: https://github.com/livinginparadise/GRDDenoiser |
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| This work introduces a novel, highly efficient denoising model featuring a compact architecture of 2 million parameters. Furthermore, a dedicated "Predictor" for poison detection of an image. |
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| The denoiser architecture prioritizes bias-free operation and responsiveness to varying noise levels. |
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| Here is how it works: |
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| * Gaussian Prior Extraction: A ResidualPriorExtractor is to extract a Gaussian prior, utilizing fixed kernels to separate high-frequency details (edges and noise) from the smooth background. This process provides initial information, highlighting regions susceptible to poisoning. |
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| * Noise Conditioning: The model incorporates a NoiseConditioner that projects the noise level (sigma) and a content descriptor into an embedding, modulating network layers. This adaptive approach adjusts the model's sensitivity based on image noise. |
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| * Bias-Free Design: All convolutional layers utilize a bias=False configuration, promoting reliance on feature data and normalization (LayerNorm2d), which is known to enhance generalization in restoration tasks. |
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| * Gated Residual Blocks: The core architecture utilizes Global Gating within Residual Blocks. A gating value (ranging from 0 to 1), derived from the global mean of features, selectively regulates information flow. |
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| The "Predictor" model, so named for its predictive capabilities, simultaneously classifies images as either poisoned or safe and predicts a noise mask indicating the location of the poison. |
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| The model employs a GhostResidualDecomposition-Net architecture to achieve high accuracy: |
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| * Backbone (ResNet + SE): The encoder utilizes Residual Blocks enhanced with Squeeze-and-Excitation (SE) Blocks, enabling channel attention and weighting of important feature maps. |
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| * ASPP (Atrous Spatial Pyramid Pooling): Located at the bottleneck, this module employs dilated convolutions to capture contextual information at multiple scales, facilitating the detection of both fine noise patterns and global image structure. |
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| * Attention Gates in the Decoder: Attention Gates are incorporated within the skip connections during image upsampling, filtering features to focus on poisoned regions. |
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| "Ghost Loss" Function |
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| Training of the predictor model utilizes a custom loss function, "Ghost Loss", to ensure the generation of realistic reconstructions. This function combines four distinct penalties: |
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| * Pixel-wise Noise Match: Measures the agreement between the predicted noise mask and the ground truth poison. |
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| * Restoration Match (MSE): Evaluates the similarity between the restored image (after mask subtraction) and the original clean image. |
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| * Binary Classification (BCE): Assesses the accuracy of the poison/safe classification. |
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| * Semantic Anchor (Perceptual Loss): A frozen VGG16 network is used to compare features of the restored image with those of the clean image, preventing excessive blurring and preserving important details. |
