Art Arena evaluates how artistic styles from training data leak into AI-generated images without explicit prompts, revealing asymmetric blending due to differences in representational strength and interaction dynamics across models like Stable Diffusion.
E-LPIPS: Robust Perceptual Image Similarity via Random Transformation Ensembles
3 Pith papers cite this work. Polarity classification is still indexing.
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abstract
It has been recently shown that the hidden variables of convolutional neural networks make for an efficient perceptual similarity metric that accurately predicts human judgment on relative image similarity assessment. First, we show that such learned perceptual similarity metrics (LPIPS) are susceptible to adversarial attacks that dramatically contradict human visual similarity judgment. While this is not surprising in light of neural networks' well-known weakness to adversarial perturbations, we proceed to show that self-ensembling with an infinite family of random transformations of the input --- a technique known not to render classification networks robust --- is enough to turn the metric robust against attack, while retaining predictive power on human judgments. Finally, we study the geometry imposed by our our novel self-ensembled metric (E-LPIPS) on the space of natural images. We find evidence of "perceptual convexity" by showing that convex combinations of similar-looking images retain appearance, and that discrete geodesics yield meaningful frame interpolation and texture morphing, all without explicit correspondences.
verdicts
UNVERDICTED 3representative citing papers
The paper argues for stateful defenses over stateless ones to detect adversarial example generation via query history and introduces query blinding as a counter-attack.
DUNE optimizes perturbations in spatial and color domains with model ensembles to produce robust unlearnable examples that reduce test accuracy to 14.95%-50.82% under 7 defenses on CIFAR-10 and ImageNet, outperforming 12 prior methods.
citing papers explorer
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The Silent Brush: Evaluating Artistic Style Leakage in AI Art Generation
Art Arena evaluates how artistic styles from training data leak into AI-generated images without explicit prompts, revealing asymmetric blending due to differences in representational strength and interaction dynamics across models like Stable Diffusion.
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Stateful Detection of Black-Box Adversarial Attacks
The paper argues for stateful defenses over stateless ones to detect adversarial example generation via query history and introduces query blinding as a counter-attack.
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Dual-branch Robust Unlearnable Examples
DUNE optimizes perturbations in spatial and color domains with model ensembles to produce robust unlearnable examples that reduce test accuracy to 14.95%-50.82% under 7 defenses on CIFAR-10 and ImageNet, outperforming 12 prior methods.