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Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger

CVPR 2023 · Adversarial robustness and security › Backdoor and poisoning · approach: Trigger-based backdoor attacks · 42 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the adaptive frequency trigger instead of LIRA's triggerCheng-Anchor compression model · Cityscapes validation, Car to Roadpixel-wise ASR (%): 56.9 -> 71.5Helped
Use the adaptive frequency trigger instead of LIRA's triggerCheng-Anchor compression model · CelebA paired face imagesface recognition accuracy (%): 35.3 -> 28.3Helped
Use the adaptive frequency trigger instead of LIRA's triggerAE-Hyperprior and Cheng-Anchor · Kodak, BPP attackbppHelped
Use the adaptive frequency trigger instead of LIRA's triggerAE-Hyperprior and Cheng-Anchor · Kodak, PSNR attackPSNRHelped
Add dynamic balance adjustment to the training loss (ablation)Adaptive frequency trigger attack · Kodak, BPP attackbppHelped
Add topK selection to general trigger generation (ablation)Adaptive frequency trigger attack · Kodak, BPP attackpoisoned-image bpp: 9.27 -> 9.45Helped
Add patch-wise weighting to the trigger (ablation)Adaptive frequency trigger attack · Kodak, BPP attackpoisoned-image bpp: 9.08 -> 9.45Helped
Use K=4 and N=16 instead of K=16 and N=64 (ablation)Adaptive frequency trigger attack · Kodak, BPP attackpoisoned-image bpp: 9.45 -> 9.08Hurt
Use K=64 and N=256 instead of K=16 and N=64 (ablation)Adaptive frequency trigger attack · Kodak, BPP attackpoisoned-image bpp: 9.45 -> 9.14Hurt

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