The article discusses the vulnerability of deep neural networks, specifically in semantic segmentation tasks, to adversarial attacks – perturbations of the input that lead to incorrect predictions. The authors propose…
Continue readingThe paper discusses the vulnerability of Large Language Models (LLMs) to jailbreak attacks that deviate them from safe behaviors, producing content misaligned with human values. The authors introduce an interpretable…
Continue readingThe paper discusses the vulnerability of multi-agent collaborative (MAC) perception, particularly in applications like autonomous driving. MAC perception, despite its advantages, is more susceptible to adversarial attacks due to the…
Continue readingThe study investigates the use of neural replay simulators to improve the robustness of over-the-air adversarial attacks on Automatic Speaker Verification (ASV) systems. ASV systems, powered by deep learning, are…
Continue readingThis academic paper discusses the vulnerability of Automatic Speaker Verification (ASV) systems built on deep neural networks to adversarial attacks. The researchers have developed an open-source adversarial attack dataset for…
Continue readingThe paper explores privacy-preserving and privacy-attacking strategies for audio and speech in voice-controlled devices. It provides an in-depth analysis of various attack and defense scenarios, highlighting the inherent susceptibility of…
Continue readingLarge Language Models (LLMs) have become an essential tool in generating high-quality text based on human prompts. However, they can potentially produce harmful content. This paper emphasizes the risk LLMs…
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