Backdoors can be embedded in ResNet and ViT models as statistically indistinguishable latent directions, reducing cryptographic undetectability to an intractable hypothesis test over parameter distributions.
Fine-pruning: Defending against backdooring attacks on deep neural networks
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ROI coding enables backdoor triggers to survive lossy compression by embedding malicious information into binary bitstreams via sample-specific or customized masks for both learned and traditional codecs.
Hammer and Anvil framework categorizes backdoors by update deviation δ and shows that principled combinations of Type-1 outlier/robust and Type-2 removal defenses resist full-information adaptive adversaries.
citing papers explorer
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Backdoor Channels Hidden in Latent Space: Cryptographic Undetectability in Modern Neural Networks
Backdoors can be embedded in ResNet and ViT models as statistically indistinguishable latent directions, reducing cryptographic undetectability to an intractable hypothesis test over parameter distributions.
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Inevitable Encounters: Backdoor Attacks Involving Lossy Compression
ROI coding enables backdoor triggers to survive lossy compression by embedding malicious information into binary bitstreams via sample-specific or customized masks for both learned and traditional codecs.
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Hammer and Anvil: Toward a Theory of Backdoors in Federated Learning
Hammer and Anvil framework categorizes backdoors by update deviation δ and shows that principled combinations of Type-1 outlier/robust and Type-2 removal defenses resist full-information adaptive adversaries.