Backdoors can be realized as statistically natural latent directions in modern neural networks, achieving high attack success with negligible clean accuracy loss and resisting existing defenses.
Borgwardt, Malte J
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KAP-CPD aggregates multiple kernels into a distribution-free change-point test for dynamic networks and supplies a fast analytic implementation.
SPIN performs bidirectional domain transfer in SBI to retain parameter mutual information from unlabeled real observations, improving real-world posterior inference under increasing misspecification.
citing papers explorer
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Backdoor Channels Hidden in Latent Space: Cryptographic Undetectability in Modern Neural Networks
Backdoors can be realized as statistically natural latent directions in modern neural networks, achieving high attack success with negligible clean accuracy loss and resisting existing defenses.
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KAP-CPD: Kernel Aggregation for Change-Point Detection in Dynamic Networks
KAP-CPD aggregates multiple kernels into a distribution-free change-point test for dynamic networks and supplies a fast analytic implementation.
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Information-Preserving Domain Transfer with Unlabeled Data in Misspecified Simulation-Based Inference
SPIN performs bidirectional domain transfer in SBI to retain parameter mutual information from unlabeled real observations, improving real-world posterior inference under increasing misspecification.