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20222024
most citedReverse Engineering of Imperceptible Adversarial Image Perturbations

8 citations · 11 across the 3 of their papers we have counts for

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cs.CV2024

UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Models

Yihua Zhang, Chongyu Fan, Yimeng Zhang +8

The technological advancements in diffusion models (DMs) have demonstrated unprecedented capabilities in text-to-image generation and are widely used in diverse applications. Howev…

cs.CV2023

MaLP: Manipulation Localization Using a Proactive Scheme

Vishal Asnani, Xi Yin, Tal Hassner +1

Advancements in the generation quality of various Generative Models (GMs) has made it necessary to not only perform binary manipulation detection but also localize the modified pix…

cs.CV20233 cited

Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment

Yiyou Sun, Yaojie Liu, Xiaoming Liu +2

This work studies the generalization issue of face anti-spoofing (FAS) models on domain gaps, such as image resolution, blurriness and sensor variations. Most prior works regard do…

cs.CV2023

Can Adversarial Examples Be Parsed to Reveal Victim Model Information?

Yuguang Yao, Jiancheng Liu, Yifan Gong +4

Numerous adversarial attack methods have been developed to generate imperceptible image perturbations that can cause erroneous predictions of state-of-the-art machine learning (ML)…

cs.CV20228 cited

Reverse Engineering of Imperceptible Adversarial Image Perturbations

Yifan Gong, Yuguang Yao, Yize Li +4

It has been well recognized that neural network based image classifiers are easily fooled by images with tiny perturbations crafted by an adversary. There has been a vast volume of…