arXiv Machine Learning
Jul 20
SODA: Semi On-Policy Black-Box Distillation for Large Language Models
arXiv:2604. 03873v4 Announce Type: replace Abstract: Black-box knowledge distillation for large language models presents a strict trade-off.
Improving the Robustness/Accuracy Tradeoff Against Adversarial Attacks Using Information Bottleneck Distillation Through Dual Teachers
arXiv:2607. 27737v1 Announce Type: new Abstract: Deep neural networks (DNNs) have achieved remarkable success in classical machine learning problems.
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arXiv AI
Sep 10
How to Backdoor Image Knowledge Distillation
The paper demonstrates that image knowledge distillation can be backdoored even when the teacher model is clean, by poisoning the distillation dataset with triggered and manipulated images that the teacher already classifies as a target label. The attack, effective at poisoning rates as low as 10%, uses targeted adversarial perturbations and GAN-based class transitions to embed a backdoor into the student model while preserving its performance on clean data. The study highlights that the security of knowledge distillation depends not only on the teacher but also on the integrity of the distillation data.
arXiv Machine Learning
Aug 5
Adversarial Purification by Consistency-aware Latent Space Optimization on Data Manifolds
arXiv:2412. 08394v2 Announce Type: replace Abstract: Deep neural networks (DNNs) are vulnerable to adversarial samples crafted by adding imperceptible perturbations to clean data, potentially leading to incorrect and dangerous predictions.
arXiv AI
Jun 2
CEAR: Certified Ensemble Adversarial Robustness in DNNs
arXiv:2606. 01437v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) are highly susceptible to adversarial perturbations, leading to extensive research on robustness for safety-critical applications.
arXiv AI
Jul 1
Improving Certified Robustness via Adversarial Distillation
arXiv:2606. 31653v1 Announce Type: cross Abstract: Certified training aims to produce models whose predictions can be formally verified against adversarial perturbations, typically by optimising upper bounds on the worst-case loss over an allowed perturbation set.
arXiv Machine Learning
Jun 2
Sensitivity as a Double-Edged Sword: A Trade-off Between Discriminability and Adversarial Robustness
arXiv:2606. 01746v1 Announce Type: cross Abstract: Modern neural networks are highly susceptible to adversarial perturbations.
arXiv Machine Learning
Aug 4
DeepDefense: Robust Learning via Layer-Wise Gradient-Feature Alignment
arXiv:2511. 13749v2 Announce Type: replace Abstract: Deep neural networks are known to be vulnerable to adversarial perturbations, which are small, carefully crafted inputs that lead to incorrect predictions.
arXiv AI
Jun 2
Visual-Noise Guided In-Context Distillation for Multimodal Large Language Model Unlearning
arXiv:2606. 00105v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress on vision-language tasks, but they may also memorize and expose sensitive or restricted knowledge, raising concerns about privacy and broader safety risks.
arXiv Machine Learning
Jul 7
Binary Iterative Method for Non-targeted Adversarial Attack
arXiv:2607. 04145v1 Announce Type: new Abstract: Adversarial attacks guide and provide additional training and test data for both adversarial training and adversarial robustness validation, and expose the 'piecewise linearity' of deep learning based models.
arXiv Machine Learning
Jun 2
A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNs
arXiv:2606. 02267v1 Announce Type: new Abstract: The vulnerability of deep neural networks to adversarial examples poses a significant challenge for real-world deployment.
arXiv AI
Jun 29
Improving Adversarial Robustness via Activation Amplification and Attenuation
arXiv:2606. 27784v1 Announce Type: cross Abstract: The existence of adversarial attacks is often attributed to the presence of non-robust features in neural networks.
arXiv Machine Learning
Jul 7
Robustness Meets Uncertainty: Evidential Adversarial Training for Robust Selective Classification
arXiv:2607. 03075v1 Announce Type: new Abstract: Safety-critical applications require classifiers that are both robust and reliable.