ImageAttributionBench is a benchmark dataset demonstrating that state-of-the-art image attribution methods lack robustness to image degradation and fail to generalize to semantically disjoint domains.
Learning to disentangle gan fingerprint for fake image attribution.arXiv preprint arXiv:2106.08749, 2021b
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DeFakeQ introduces an adaptive bidirectional quantization method tailored for deepfake detectors that maintains detection accuracy while enabling real-time performance on resource-constrained edge devices.
Orthogonal subspace decomposition via SVD on vision foundation model features preserves high-rank pre-trained knowledge by freezing principal components and adapting residuals, reducing overfitting for better generalization in AI-generated image detection.
citing papers explorer
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ImageAttributionBench: How Far Are We from Generalizable Attribution?
ImageAttributionBench is a benchmark dataset demonstrating that state-of-the-art image attribution methods lack robustness to image degradation and fail to generalize to semantically disjoint domains.
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DeFakeQ: Enabling Real-Time Deepfake Detection on Edge Devices via Adaptive Bidirectional Quantization
DeFakeQ introduces an adaptive bidirectional quantization method tailored for deepfake detectors that maintains detection accuracy while enabling real-time performance on resource-constrained edge devices.
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Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection
Orthogonal subspace decomposition via SVD on vision foundation model features preserves high-rank pre-trained knowledge by freezing principal components and adapting residuals, reducing overfitting for better generalization in AI-generated image detection.