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Exploring Adversarial Attacks against Latent Diffusion Model from the Perspective of Adversarial Transferability
Recently, many studies utilized adversarial examples (AEs) to raise the cost of malicious image editing and copyright violation powered by latent diffusion models (LDMs).
Releasing Inequality Phenomena in $L_{\infty}$-Adversarial Training via Input Gradient Distillation
However, a recent work showed the inequality phenomena in $l_{\infty}$-adversarial training and revealed that the $l_{\infty}$-adversarially trained model is vulnerable when a few important pixels are perturbed by i. i. d.
Adversarial Attack and Defense for Medical Image Analysis: Methods and Applications
In this exposition, we present a comprehensive survey on recent advances in adversarial attack and defense for medical image analysis with a novel taxonomy in terms of the application scenario.
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