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07/17/2023
DeepMem: ML Models as storage channels and their (mis-)applications
Machine learning (ML) models are overparameterized to support generality...
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05/19/2023
DAP: A Dynamic Adversarial Patch for Evading Person Detectors
In this paper, we present a novel approach for generating naturalistic a...
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04/20/2023
Jedi: Entropy-based Localization and Removal of Adversarial Patches
Real-world adversarial physical patches were shown to be successful in c...
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03/03/2023
Exploring Machine Learning Privacy/Utility trade-off from a hyperparameters Lens
Machine Learning (ML) architectures have been applied to several applica...
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03/03/2023
AdvART: Adversarial Art for Camouflaged Object Detection Attacks
A majority of existing physical attacks in the real world result in cons...
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03/02/2023
APARATE: Adaptive Adversarial Patch for CNN-based Monocular Depth Estimation for Autonomous Navigation
In recent years, monocular depth estimation (MDE) has witnessed a substa...
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11/02/2022
Defending with Errors: Approximate Computing for Robustness of Deep Neural Networks
Machine-learning architectures, such as Convolutional Neural Networks (C...
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11/02/2022
a-RNA: Adversarial Radio Noise Attack to Fool Radar-based Environment Perception Systems
Due to their robustness to degraded capturing conditions, radars are wid...
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04/18/2022
Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems
The real-world use cases of Machine Learning (ML) have exploded over the...
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01/05/2022
ROOM: Adversarial Machine Learning Attacks Under Real-Time Constraints
Advances in deep learning have enabled a wide range of promising applica...
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10/10/2021
Adversarial Attacks in a Multi-view Setting: An Empirical Study of the Adversarial Patches Inter-view Transferability
While machine learning applications are getting mainstream owing to a de...
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07/27/2021
PDF-Malware: An Overview on Threats, Detection and Evasion Attacks
In the recent years, Portable Document Format, commonly known as PDF, ha...
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03/11/2021
Stochastic-HMDs: Adversarial Resilient Hardware Malware Detectors through Voltage Over-scaling
Machine learning-based hardware malware detectors (HMDs) offer a potenti...
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01/05/2021
An Investigation on Inherent Robustness of Posit Data Representation
As the dimensions and operating voltages of computer electronics shrink ...
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12/09/2020
Securing Deep Spiking Neural Networks against Adversarial Attacks through Inherent Structural Parameters
Deep Learning (DL) algorithms have gained popularity owing to their prac...
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06/13/2020
Defensive Approximation: Enhancing CNNs Security through Approximate Computing
In the past few years, an increasing number of machine-learning and deep...
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05/16/2020
