Inevitable Encounters: Backdoor Attacks Involving Lossy Compression
Qian Li, Yunuo Chen, Yuntian Chen

TL;DR
This paper investigates how lossy compression affects backdoor attacks in deep learning, proposing new strategies to embed triggers into compressed images to maintain attack effectiveness.
Contribution
It introduces two novel poisoning strategies that ensure malicious triggers survive lossy compression, addressing a key challenge in real-world backdoor attacks.
Findings
Lossy compression often destroys backdoor triggers in images.
Proposed strategies successfully embed triggers into compressed images.
Experiments confirm the effectiveness of the new poisoning methods.
Abstract
Real-world backdoor attacks often require poisoned datasets to be stored and transmitted before being used to compromise deep learning systems. However, in the era of big data, the inevitable use of lossy compression poses a fundamental challenge to invisible backdoor attacks. We find that triggers embedded in RGB images often become ineffective after the images are lossily compressed into binary bitstreams (e.g., JPEG files) for storage and transmission. As a result, the poisoned data lose its malicious effect after compression, causing backdoor injection to fail. In this paper, we highlight the necessity of explicitly accounting for the lossy compression process in backdoor attacks. This requires attackers to ensure that the transmitted binary bitstreams preserve malicious trigger information, so that effective triggers can be recovered in the decompressed data. Building on the…
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Taxonomy
TopicsDigital Media Forensic Detection · Adversarial Robustness in Machine Learning · Advanced Data Compression Techniques
