The paper discusses adversarial training’s robustness-accuracy trade-off problem and proposes a solution. The authors focus on invariance regularization to create adversarially invariant representations without losing discriminative power. They identify two…
Continue readingThis study investigates adversarial attacks through the lens of diffusion models, but not for enhancing the adversarial robustness of image classifiers. The focus is on utilizing the diffusion model to…
Continue readingThe research paper discusses the vulnerability of Deep Neural Networks (DNNs) to adversarial examples. Traditional methods assume that attackers can modify any features, neglecting the causal generating process of data,…
Continue readingThis academic paper discusses the role of data augmentation in improving the transferability of adversarial examples (AEs) in machine learning. The authors systematically studied 46 augmentation techniques and their effects…
Continue readingThe paper explores a scenario where a side-channel attack is used to break the black-box property of embedded artificial intelligence, specifically deep neural networks (DNNs). It presents an architecture-agnostic attack…
Continue readingThe paper by Oliver Turnbull and George Cevora from Artefact Ltd. discusses the inherent instability of computer vision models. The authors argue that adversarial examples, which are small changes to…
Continue readingThe article is an extensive survey on the transferability of adversarial examples in Deep Neural Networks (DNNs). It discusses the vulnerability of DNNs to adversarial examples – inputs that can…
Continue readingThis research presents a novel image classification scheme that preserves privacy by using a feature extractor and an encoder to mask the plaintext image through a Noise-like Adversarial Example (NAE)….
Continue readingThe article revisits the transferable adversarial examples, which are a significant security concern in real-world, black-box attack scenarios. The authors identify two main problems in common evaluation practices: lack of…
Continue readingThe research focuses on efficient score-based black-box adversarial attacks with a high Attack Success Rate (ASR) and good generalizability. The authors propose a novel attack method, DifAttack, which operates over…
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