1.5k citations · 3.1k across the 169 of their papers we have counts for
21 papers · 1 filter
BENN: Bias Estimation Using Deep Neural Network
Amit Giloni, Edita Grolman, Tanja Hagemann +4
The need to detect bias in machine learning (ML) models has led to the development of multiple bias detection methods, yet utilizing them is challenging since each method: i) explo…
The Translucent Patch: A Physical and Universal Attack on Object Detectors
Alon Zolfi, Moshe Kravchik, Yuval Elovici +1
Physical adversarial attacks against object detectors have seen increasing success in recent years. However, these attacks require direct access to the object of interest in order…
Detection of Adversarial Supports in Few-shot Classifiers Using Self-Similarity and Filtering
Yi Xiang Marcus Tan, Penny Chong, Jiamei Sun +3
Few-shot classifiers excel under limited training samples, making them useful in applications with sparsely user-provided labels. Their unique relative prediction setup offers oppo…
Toward Scalable and Unified Example-based Explanation and Outlier Detection
Penny Chong, Ngai-Man Cheung, Yuval Elovici +1
When neural networks are employed for high-stakes decision-making, it is desirable that they provide explanations for their prediction in order for us to understand the features th…
Being Single Has Benefits. Instance Poisoning to Deceive Malware Classifiers
Tzvika Shapira, David Berend, Ishai Rosenberg +3
The performance of a machine learning-based malware classifier depends on the large and updated training set used to induce its model. In order to maintain an up-to-date training s…
Dynamic Adversarial Patch for Evading Object Detection Models
Shahar Hoory, Tzvika Shapira, Asaf Shabtai +1
Recent research shows that neural networks models used for computer vision (e.g., YOLO and Fast R-CNN) are vulnerable to adversarial evasion attacks. Most of the existing real-worl…