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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Posts
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portfolio
Defensive Approximation: Securing CNNs using Approximate Computing
Published:
We show that approximate computing can act as a security primitive by injecting input-dependent noise into neural computations. Defensive Approximation leverages hardware-level approximation to disrupt adversarial transferability while improving efficiency.
SSAP: A Shape-Sensitive Adversarial Patch for Monocular Depth Estimation
Published:
A shape-aware adversarial patch that reveals fundamental vulnerabilities in geometric perception systems.
DAP: A Dynamic Adversarial Patch for Evading Person Detectors
Published:
A dynamic adversarial patch that generates printable, naturalistic patterns robust to pose changes, clothing deformation, and real-world transformations.
ODDR: Outlier Detection & Dimension Reduction Based Defense Against Adversarial Patch Attacks
Published:
We show that adversarial patches dominate predictions by creating outlier feature activations. ODDR detects and suppresses these anomalies through feature-space outlier modeling and dimension reduction, restoring robust predictions.
TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization
Published:
A framework that significantly improves black-box adversarial attack transferability from Vision Transformers via spectral and semantic regularization.
DRIFT: Divergent Response in Filtered Transformations for Robust Adversarial Defense
Published:
A stochastic differentiable defense framework that breaks gradient consensus via divergent responses across filtered transformations.
TriQDef: Disrupting Semantic and Gradient Alignment to Prevent Adversarial Patch Transferability in Quantized Neural Networks
Published:
A defense framework that disrupts cross-bit structural alignment to prevent patch transferability in quantized neural networks.
Do Not Leave a Gap: Hallucination-Free Object Concealment in Vision-Language Models
Published:
We show that hallucination in vision-language models is caused by representational discontinuity—not object absence. We introduce Background-Consistent Re-encoding (BCR), which conceals objects by aligning their representations with the background while preserving token structure and attention flow.
publications
HEAP: A Heterogeneous Approximate Floating-Point Multiplier for Error Tolerant Applications
Published in The International Workshop on Rapid System Prototyping (RSP), 2019, New York NY USA, 2019
Recommended citation: Amira Guesmi, Ihsen Alouani, Mouna Baklouti, Tarek Frikha, Mohamed Abid, and Atika Rivenq
Defensive approximation: securing CNNs using approximate computing
Published in The ACM international conference on architectural support for programming languages and operating systems (ASPLOS), 2021, USA, 2021
Recommended citation: Amira Guesmi, Ihsen Alouani, Khaled Khasawneh, Mouna Baklouti, Tarek Frikha, Mohamed Abid, Nael Abu-Ghazaleh
SIT: Stochastic Input Transformation to Defend Against Adversarial Attacks on Deep Neural Networks
Published in The IEEE Design & Test, 2022, 2022
Recommended citation: Amira Guesmi, Ihsen Alouani, Mouna Baklouti, Tarek Frikha, Mohamed Abid
Towards an agile design methodology for efficient, reliable, and secure ML systems
Published in The IEEE VLSI Test Symposium (VTS), 2022, San Diego, CA, USA, 2022
Recommended citation: Shail Dave, Alberto Marchisio, Muhammad Abdullah Hanif, Amira Guesmi, Aviral Shrivastava, Ihsen Alouani, Muhammad Shafique
ROOM: Adversarial Machine Learning Attacks Under Real-Time Constraints
Published in International Joint Conference on Neural Networks (IJCNN), 2022, Padua, Italy, 2022
Recommended citation: Amira Guesmi, Khaled N. Khasawneh, Nael Abu-Ghazaleh, Ihsen Alouani'
Defending with Errors: Approximate Computing for Robustness of Deep Neural Networks
Published in ArXiv, 2022
Recommended citation: Amira Guesmi, Ihsen Alouani, Khaled N. Khasawneh, Mouna Baklouti, Tarek Frikha, Mohamed Abid, Nael Abu-Ghazaleh
Adversarial Attack on Radar-based Environment Perception Systems
Published in ArXiv, 2022
Recommended citation: Amira Guesmi, Ihsen Alouani
Exploring Machine Learning Privacy/Utility Trade-Off from a Hyperparameters Lens
Published in International Joint Conference on Neural Networks (IJCNN), 2023, Queensland, Australia, 2023
Recommended citation: Ayoub Arous, Amira Guesmi, Muhammad Abdullah Hanif, Muhammad Shafique
AaN: Anti-adversarial Noise - A Novel Approach for Securing Machine Learning-based Wireless Communication Systems
Published in ArXiv, 2023
Recommended citation: Anis Amazigh Hamza, Amira Guesmi, Iyad Dayoub, Abderrahmane Amrouche, Ihsen Alouani
ODDR: Outlier Detection & Dimension Reduction Based Defense Against Adversarial Patches
Published in ArXiv, 2023
Recommended citation: Nandish Chattopadhyay, Amira Guesmi, Muhammad Abdullah Hanif, Bassem Ouni, Muhammad Shafique
Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook
Published in The IEEE Access, 2023, 2023
Recommended citation: Amira Guesmi, Muhammad Abdullah Hanif, Bassem Ouni, Muhammad Shafique
SAAM: Stealthy Adversarial Attack on Monocular Depth Estimation
Published in The IEEE Access, 2024, 2024
Recommended citation: Amira Guesmi, Muhammad Abdullah Hanif, Bassem Ouni, Muhammad Shafique
Anomaly Unveiled: Securing Image Classification against Adversarial Patch Attacks
Published in The IEEE International Conference on Image Processing (ICIP) 2024, Abu Dhabi, UAE, 2024
Recommended citation: Nandish Chattopadhyay, Amira Guesmi, Muhammad Shafique
AdvART: Adversarial Art for Camouflaged Object Detection Attacks
Published in The IEEE International Conference on Image Processing (ICIP) 2024, Abu Dhabi, UAE, 2024
Recommended citation: Amira Guesmi, Ioan Marius Bilasco, Muhammad Shafique, Ihsen Alouani
Exploring the Interplay of Interpretability and Robustness in Deep Neural Networks: A Saliency-guided Approach
Published in ICIP: Security and Privacy of Machine Learning-based Vision Processing in Autonomous Systems (SPVis), 2024, Abu Dhabi, UAE, 2024
Recommended citation: Amira Guesmi, Nishant Suresh Aswani, Muhammad Shafique
Dap: A dynamic adversarial patch for evading person detectors
Published in The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) 2024, Seattle, Washington, USA, 2024
Recommended citation: Amira Guesmi, Ruitian Ding, Muhammad Abdullah Hanif, Ihsen Alouani, Muhammad Shafique
DefensiveDR: Defending against Adversarial Patches using Dimensionality Reduction
Published in The ACM/IEEE Design Automation Conference (DAC), 2024, San Francisco, USA, 2024
Recommended citation: Nandish Chattopadhyay, Amira Guesmi, Muhammad Abdullah Hanif, Bassem Ouni, Muhammad Shafique
SSAP: A Shape-Sensitive Adversarial Patch for Comprehensive Disruption of Monocular Depth Estimation in Autonomous Navigation Applications
Published in The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2024, Abu Dhabi, UAE, 2024
Recommended citation: Amira Guesmi, Muhammad Abdullah Hanif, Ihsen Alouani, Bassem Ouni, Muhammad Shafique
Examining Changes in Internal Representations of Continual Learning Models Through Tensor Decomposition
Published in ContinualAI Unconference, 2023, Virtual, 2024
Recommended citation: Nishant Suresh Aswani, Amira Guesmi, Muhammad Abdullah Hanif, Muhammad Shafique
AdvRain: Adversarial Raindrops to Attack Camera-Based Smart Vision Systems
Published in Information, 2023, 2024
Recommended citation: Amira Guesmi, Muhammad Abdullah Hanif, Bassem Ouni, Muhammad Shafique
talks
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.