In the proportional high-dimensional regime, stronger backdoor training triggers improve clean accuracy and make attack success non-monotonic for regularized GLMs on Gaussian mixtures, with closed-form proofs for squared loss and fixed-point extensions to convex losses.
Label-consistent back- door attacks
7 Pith papers cite this work. Polarity classification is still indexing.
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HTell detects backdoors by random probing of the model head, reporting 99.03% true positive rate and 2.11% false positive rate at 12.69 ms per model on a benchmark of over 6700 models.
Backdoor attacks on VLM-based scanpath predictors can redirect fixations toward chosen objects or inflate durations using input-conditioned triggers that evade cluster detection, and no tested defense blocks them without hurting clean accuracy.
SABLE shows that semantics-aware natural triggers enable effective backdoor attacks in federated learning against multiple aggregation rules while preserving benign accuracy.
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.
Checkerboard derives a closed-form checkerboard trigger for clean-label backdoor attacks that achieves over 94% ASR with poisoning rates as low as 0.46% on ImageNet-100 and 99.99% ASR with 20 samples on CIFAR-10.
CSC identifies backdoored samples via early-epoch latent clustering and conceals them by relabeling to a virtual class, driving attack success rates near zero on benchmarks with little clean accuracy loss.
citing papers explorer
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When Stronger Triggers Backfire: A High-Dimensional Theory of Backdoor Attacks
In the proportional high-dimensional regime, stronger backdoor training triggers improve clean accuracy and make attack success non-monotonic for regularized GLMs on Gaussian mixtures, with closed-form proofs for squared loss and fixed-point extensions to convex losses.
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Fast and Lightweight Backdoor Detection via Head Random Probing
HTell detects backdoors by random probing of the model head, reporting 99.03% true positive rate and 2.11% false positive rate at 12.69 ms per model on a benchmark of over 6700 models.
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Follow My Eyes: Backdoor Attacks on VLM-based Scanpath Prediction
Backdoor attacks on VLM-based scanpath predictors can redirect fixations toward chosen objects or inflate durations using input-conditioned triggers that evade cluster detection, and no tested defense blocks them without hurting clean accuracy.
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Beyond Corner Patches: Semantics-Aware Backdoor Attack in Federated Learning
SABLE shows that semantics-aware natural triggers enable effective backdoor attacks in federated learning against multiple aggregation rules while preserving benign accuracy.
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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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Checkerboard: A Simple, Effective, Efficient and Learning-free Clean Label Backdoor Attack with Low Poisoning Budget
Checkerboard derives a closed-form checkerboard trigger for clean-label backdoor attacks that achieves over 94% ASR with poisoning rates as low as 0.46% on ImageNet-100 and 99.99% ASR with 20 samples on CIFAR-10.
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CSC: Turning the Adversary's Poison against Itself
CSC identifies backdoored samples via early-epoch latent clustering and conceals them by relabeling to a virtual class, driving attack success rates near zero on benchmarks with little clean accuracy loss.