FlowHijack is the first dynamics-aware backdoor attack on flow-matching VLAs that achieves high success rates with stealthy triggers while preserving benign performance and making malicious actions kinematically indistinguishable from normal ones.
Neural attention distillation: Erasing backdoor triggers from deep neural networks
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
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BadSNN injects backdoors into spiking neural networks by adversarially tuning LIF neuron hyperparameters and optimizing triggers, achieving higher attack success than prior data-poisoning methods while remaining robust to common defenses.
GSPure removes watermarks from 3D Gaussian Splatting by isolating watermark Gaussians via view-dependent contributions and feature clustering, cutting watermark PSNR by up to 16.34 dB with under 1 dB scene quality loss.
Landseer offers a containerized modular system to integrate and evaluate combinations of machine learning defenses, with an initial analysis of 35 defenses highlighting replicability challenges.
MIST detects Trojaned DNN updates by measuring spectral deviations in pre-activation representations against a benign fine-tuning reference, achieving high accuracy across datasets and attacks after a single update.
TIGS detects backdoor-induced attention collapse in LLMs and applies content-aware tail-risk screening plus intrinsic geometric smoothing to suppress attacks while preserving normal performance.
A complete pipeline for federated unlearning via knowledge distillation for efficient removal and a GAN-integrated classifier for visual evaluation of forgetting capacity.
citing papers explorer
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Forgetting to Witness: Efficient Federated Unlearning and Its Visible Evaluation
A complete pipeline for federated unlearning via knowledge distillation for efficient removal and a GAN-integrated classifier for visual evaluation of forgetting capacity.