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Found 13 papers, 1 papers with code
SSAP: A Shape-Sensitive Adversarial Patch for Comprehensive Disruption of Monocular Depth Estimation in Autonomous Navigation Applications
In this paper, we introduce SSAP (Shape-Sensitive Adversarial Patch), a novel approach designed to comprehensively disrupt monocular depth estimation (MDE) in autonomous navigation applications.
Anomaly Unveiled: Securing Image Classification against Adversarial Patch Attacks
Adversarial patch attacks pose a significant threat to the practical deployment of deep learning systems.
ODDR: Outlier Detection & Dimension Reduction Based Defense Against Adversarial Patches
ODDR employs a three-stage pipeline: Fragmentation, Segregation, and Neutralization, providing a model-agnostic solution applicable to both image classification and object detection tasks.
Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook
Through this comprehensive survey, we aim to provide a valuable resource for researchers, practitioners, and policymakers to gain a holistic understanding of physical adversarial attacks in computer vision and facilitate the development of robust and secure DNN-based systems.
SAAM: Stealthy Adversarial Attack on Monocular Depth Estimation
In this paper, we investigate the vulnerability of MDE to adversarial patches.
DAP: A Dynamic Adversarial Patch for Evading Person Detectors
Patch-based adversarial attacks were proven to compromise the robustness and reliability of computer vision systems.
Exploring Machine Learning Privacy/Utility trade-off from a hyperparameters Lens
Towards investigating new ground for better privacy-utility trade-off, this work questions; (i) if models' hyperparameters have any inherent impact on ML models' privacy-preserving properties, and (ii) if models' hyperparameters have any impact on the privacy/utility trade-off of differentially private models.
AdvART: Adversarial Art for Camouflaged Object Detection Attacks
Physical adversarial attacks pose a significant practical threat as it deceives deep learning systems operating in the real world by producing prominent and maliciously designed physical perturbations.
APARATE: Adaptive Adversarial Patch for CNN-based Monocular Depth Estimation for Autonomous Navigation
APARATE, results in a mean depth estimation error surpassing $0. 5$, significantly impacting as much as $99\%$ of the targeted region when applied to CNN-based MDE models.
AdvRain: Adversarial Raindrops to Attack Camera-based Smart Vision Systems
Unlike mask based fake-weather attacks that require access to the underlying computing hardware or image memory, our attack is based on emulating the effects of a natural weather condition (i. e., Raindrops) that can be printed on a translucent sticker, which is externally placed over the lens of a camera.
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 past few years.
ROOM: Adversarial Machine Learning Attacks Under Real-Time Constraints
Thus, we propose ROOM, a novel Real-time Online-Offline attack construction Model where an offline component serves to warm up the online algorithm, making it possible to generate highly successful attacks under time constraints.
Defensive Approximation: Securing CNNs using Approximate Computing
We show that our approximate computing implementation achieves robustness across a wide range of attack scenarios.
