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RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detection
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RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detection
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The rapid advances in generative AI models have empowered the creation of highly realistic images with arbitrary content, raising concerns about potential misuse and harm, such as Deepfakes. Current research focuses on training detectors using large datasets of generated images. However, these training-based solutions are often computationally expensive and show limited generalization to unseen generated images. In this paper, we propose a training-free method to distinguish between real and AI-generated images. We first observe that real images are more robust to tiny noise perturbations than AI-generated images in the representation space of vision foundation models. Based on this observation, we propose RIGID, a training-free and model-agnostic method for robust AI-generated image detection. RIGID is a simple yet effective approach that identifies whether an image is AI-generated by comparing the representation similarity between the original and the noise-perturbed counterpart. Our evaluation on a diverse set of AI-generated images and benchmarks shows that RIGID significantly outperforms existing trainingbased and training-free detectors. In particular, the average performance of RIGID exceeds the current best training-free method by more than 25%. Importantly, RIGID exhibits strong generalization across different image generation methods and robustness to image corruptions.
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Cited by 16 Pith papers
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How Fragile Are Training-Free AI-Generated Image Detectors? A Controlled Audit of Score Direction, Preprocessing, and Compression
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Noise residual multi-scale ViT features plus real-prior K-Means detect synthetic images training-free at ~86% average accuracy, strongest on diffusion models across four benchmarks.
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Intermediate Representations are Strong AI-Generated Image Detectors
Intermediate layer embedding sensitivity to perturbations distinguishes AI-generated images from real ones, yielding higher AUROC on GenImage and Forensics Small benchmarks than prior methods.
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Findings of the Counter Turing Test: AI-Generated Image Detection
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Findings of the Counter Turing Test: AI-Generated Image Detection
Binary AI vs. real image classification reaches F1 > 0.83 while identifying the exact generative model achieves a highest F1 of 0.4986 on the MS COCOAI dataset.
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