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Towards trustworthy and aligned machine learning: A data-centric survey with causality perspectives
H Liu, M Chaudhary, H Wang - ar** safe autonomous vehicles.
Although previous works have studied adversarial robustness in the context of trajectory …
Although previous works have studied adversarial robustness in the context of trajectory …
Resilience and security of deep neural networks against intentional and unintentional perturbations: Survey and research challenges
In order to deploy deep neural networks (DNNs) in high-stakes scenarios, it is imperative
that DNNs provide inference robust to external perturbations-both intentional and …
that DNNs provide inference robust to external perturbations-both intentional and …
Revisiting the adversarial robustness of vision language models: a multimodal perspective
Pretrained vision-language models (VLMs) like CLIP exhibit exceptional generalization
across diverse downstream tasks. While recent studies reveal their vulnerability to …
across diverse downstream tasks. While recent studies reveal their vulnerability to …
Attention-based investigation and solution to the trade-off issue of adversarial training
Adversarial training has become the mainstream method to boost adversarial robustness of
deep models. However, it often suffers from the trade-off dilemma, where the use of …
deep models. However, it often suffers from the trade-off dilemma, where the use of …
Artificial Immune System of Secure Face Recognition Against Adversarial Attacks
Deep learning-based face recognition models are vulnerable to adversarial attacks. In
contrast to general noises, the presence of imperceptible adversarial noises can lead to …
contrast to general noises, the presence of imperceptible adversarial noises can lead to …
On the limitations of adversarial training for robust image classification with convolutional neural networks
Adversarial Training has proved to be an effective training paradigm to enforce robustness
against adversarial examples in modern neural network architectures. Despite many efforts …
against adversarial examples in modern neural network architectures. Despite many efforts …