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Found 5 papers, 0 papers with code
Attention Eclipse: Manipulating Attention to Bypass LLM Safety-Alignment
Recent research has shown that carefully crafted jailbreak inputs can induce large language models to produce harmful outputs, despite safety measures such as alignment.
Fool the Hydra: Adversarial Attacks against Multi-view Object Detection Systems
Adversarial patches exemplify the tangible manifestation of the threat posed by adversarial attacks on Machine Learning (ML) models in real-world scenarios.
Attention Deficit is Ordered! Fooling Deformable Vision Transformers with Collaborative Adversarial Patches
The latest generation of transformer-based vision models has proven to be superior to Convolutional Neural Network (CNN)-based models across several vision tasks, largely attributed to their remarkable prowess in relation modeling.
Learn to Compress (LtC): Efficient Learning-based Streaming Video Analytics
In this paper, we introduce LtC, a collaborative framework between the video source and the analytics server, that efficiently learns to reduce the video streams within an analytics pipeline.
Co(ve)rtex: ML Models as storage channels and their (mis-)applications
In this paper, we propose a new information theoretic perspective of the problem; we consider the ML model as a storage channel with a capacity that increases with overparameterization.
