3 papers
cs.LG2025
Breaking the Illusion: Consensus-Based Generative Mitigation of Adversarial Illusions in Multi-Modal Embeddings
Fatemeh Akbarian, Anahita Baninajjar, Yingyi Zhang +2
Multi-modal foundation models align images, text, and other modalities in a shared embedding space but remain vulnerable to adversarial illusions [35], where imperceptible perturba…
cs.LG2024
Formal Local Implication Between Two Neural Networks
Anahita Baninajjar, Ahmed Rezine, Amir Aminifar
Given two neural network classifiers with the same input and output domains, our goal is to compare the two networks in relation to each other over an entire input region (e.g., wi…
cs.LG2023
VNN: Verification-Friendly Neural Networks with Hard Robustness Guarantees
Anahita Baninajjar, Ahmed Rezine, Amir Aminifar
Machine learning techniques often lack formal correctness guarantees, evidenced by the widespread adversarial examples that plague most deep-learning applications. This lack of for…