5 citations · 6 across the 5 of their papers we have counts for
5 papers
DRIFT: Divergent Response in Filtered Transformations for Robust Adversarial Defense
Amira Guesmi, Muhammad Shafique
Deep neural networks remain highly vulnerable to adversarial examples, and most defenses collapse once gradients can be reliably estimated. We identify \emph{gradient consensus} --…
TriQDef: Disrupting Semantic and Gradient Alignment to Prevent Adversarial Patch Transferability in Quantized Neural Networks
Amira Guesmi, Bassem Ouni, Muhammad Shafique
Quantized Neural Networks (QNNs) are increasingly deployed in edge and resource-constrained environments due to their efficiency in computation and memory usage. While shown to dis…
Anomaly Unveiled: Securing Image Classification against Adversarial Patch Attacks
Nandish Chattopadhyay, Amira Guesmi, Muhammad Shafique
Adversarial patch attacks pose a significant threat to the practical deployment of deep learning systems. However, existing research primarily focuses on image pre-processing defen…
Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook
Amira Guesmi, Muhammad Abdullah Hanif, Bassem Ouni +1
In this paper, we present a comprehensive survey of the current trends focusing specifically on physical adversarial attacks. We aim to provide a thorough understanding of the conc…
Exploring Machine Learning Privacy/Utility trade-off from a hyperparameters Lens
Ayoub Arous, Amira Guesmi, Muhammad Abdullah Hanif +2
Machine Learning (ML) architectures have been applied to several applications that involve sensitive data, where a guarantee of users' data privacy is required. Differentially Priv…