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Found 8 papers, 2 papers with code
Unveiling Vulnerabilities in Interpretable Deep Learning Systems with Query-Efficient Black-box Attacks
Deep learning has been rapidly employed in many applications revolutionizing many industries, but it is known to be vulnerable to adversarial attacks.
Microbial Genetic Algorithm-based Black-box Attack against Interpretable Deep Learning Systems
Our results show that the proposed approach is query-efficient with a high attack success rate that can reach between 95% and 100% and transferability with an average success rate of 69% in the ImageNet and CIFAR datasets.
Single-Class Target-Specific Attack against Interpretable Deep Learning Systems
The universal perturbation is stochastically and iteratively optimized by minimizing the adversarial loss that is designed to consider both the classifier and interpreter costs in targeted and non-targeted categories.
SHIELD: Thwarting Code Authorship Attribution
For the targeted attacks, we show the possibility of impersonating a programmer using targeted-adversarial perturbations with a success rate ranging from 66\% to 88\% for different authorship attribution techniques under several adversarial scenarios.
Interpretations Cannot Be Trusted: Stealthy and Effective Adversarial Perturbations against Interpretable Deep Learning
We assess the effectiveness of proposed attacks against two deep learning model architectures coupled with four interpretation models that represent different categories of interpretation models.
Hate, Obscenity, and Insults: Measuring the Exposure of Children to Inappropriate Comments in YouTube
Social media has become an essential part of the daily routines of children and adolescents.
Sensor-based Continuous Authentication of Smartphones' Users Using Behavioral Biometrics: A Contemporary Survey
This task is made possible with today's smartphones' embedded sensors that enable continuous and implicit user authentication by capturing behavioral biometrics and traits.
W-Net: A CNN-based Architecture for White Blood Cells Image Classification
Computer-aided methods for analyzing white blood cells (WBC) have become widely popular due to the complexity of the manual process.
