ME: Trigger Element Combination Backdoor Attack on Copyright Infringement
Feiyu Yang, Siyuan Liang, Aishan Liu, Dacheng Tao

TL;DR
This paper introduces the Multi-Element (ME) attack method based on SilentBadDiffusion (SBD) to improve copyright infringement attacks on diffusion models, utilizing new datasets, multiple poisoned elements, and DCT for stealthiness, achieving higher attack success rates.
Contribution
Proposes the ME attack method with multiple poisoned elements and DCT to enhance attack effectiveness and stealthiness on diffusion models, addressing dataset limitations.
Findings
Achieved high copyright infringement rates on new datasets.
Outperformed baseline SBD in low-sampling scenarios.
Demonstrated improved stealthiness with DCT.
Abstract
The capability of generative diffusion models (DMs) like Stable Diffusion (SD) in replicating training data could be taken advantage of by attackers to launch the Copyright Infringement Attack, with duplicated poisoned image-text pairs. SilentBadDiffusion (SBD) is a method proposed recently, which shew outstanding performance in attacking SD in text-to-image tasks. However, the feasible data resources in this area are still limited, some of them are even constrained or prohibited due to the issues like copyright ownership or inappropriate contents; And not all of the images in current datasets are suitable for the proposed attacking methods; Besides, the state-of-the-art (SoTA) performance of SBD is far from ideal when few generated poisoning samples could be adopted for attacks. In this paper, we raised new datasets accessible for researching in attacks like SBD, and proposed…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Generative Adversarial Networks and Image Synthesis · Digital Media Forensic Detection
MethodsDiffusion · Discrete Cosine Transform
