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.
Deepfool: a simple and accurate method to fool deep neural networks
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A framework models DNN layer weight-activation interactions via Bernoulli distributions and uses class separation as a diagnostic proxy to quantify distributional robustness, tested on CIFAR-10 and ImageNet models.
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BadSNN: Backdoor Attacks on Spiking Neural Networks via Adversarial Spiking Neuron
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.
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A New Framework to Analyse the Distributional Robustness of Deep Neural Networks
A framework models DNN layer weight-activation interactions via Bernoulli distributions and uses class separation as a diagnostic proxy to quantify distributional robustness, tested on CIFAR-10 and ImageNet models.
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