2 citations · 2 across the 5 of their papers we have counts for
5 papers
Attention Eclipse: Manipulating Attention to Bypass LLM Safety-Alignment
Pedram Zaree, Md Abdullah Al Mamun, Quazi Mishkatul Alam +3
Recent research has shown that carefully crafted jailbreak inputs can induce large language models to produce harmful outputs, despite safety measures such as alignment. It is impo…
Attention Deficit is Ordered! Fooling Deformable Vision Transformers with Collaborative Adversarial Patches
Quazi Mishkatul Alam, Bilel Tarchoun, Ihsen Alouani +1
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 attribute…
Fool the Hydra: Adversarial Attacks against Multi-view Object Detection Systems
Bilel Tarchoun, Quazi Mishkatul Alam, Nael Abu-Ghazaleh +1
Adversarial patches exemplify the tangible manifestation of the threat posed by adversarial attacks on Machine Learning (ML) models in real-world scenarios. Robustness against thes…
Learn to Compress (LtC): Efficient Learning-based Streaming Video Analytics
Quazi Mishkatul Alam, Israat Haque, Nael Abu-Ghazaleh
Video analytics are often performed as cloud services in edge settings, mainly to offload computation, and also in situations where the results are not directly consumed at the vid…
Co(ve)rtex: ML Models as storage channels and their (mis-)applications
Md Abdullah Al Mamun, Quazi Mishkatul Alam, Erfan Shayegani +3
Machine learning (ML) models are overparameterized to support generality and avoid overfitting. The state of these parameters is essentially a "don't-care" with respect to the prim…