11 papers
Feature-Space Bayesian Adversarial Learning Improved Malware Detector Robustness
Bao Gia Doan, Shuiqiao Yang, Paul Montague +6
We present a new algorithm to train a robust malware detector. Modern malware detectors rely on machine learning algorithms. Now, the adversarial objective is to devise alterations…
Label Shift Estimation With Incremental Prior Update
Yunrui Zhang, Gustavo Batista, Salil S. Kanhere
An assumption often made in supervised learning is that the training and testing sets have the same label distribution. However, in real-life scenarios, this assumption rarely hold…
In Vino Veritas and Vulnerabilities: Examining LLM Safety via Drunk Language Inducement
Anudeex Shetty, Aditya Joshi, Salil S. Kanhere
Humans are susceptible to undesirable behaviours and privacy leaks under the influence of alcohol. This paper investigates drunk language, i.e., text written under the influence of…
Demo: TOSense -- What Did You Just Agree to?
Xinzhang Chen, Hassan Ali, Arash Shaghaghi +2
Online services often require users to agree to lengthy and obscure Terms of Service (ToS), leading to information asymmetry and legal risks. This paper proposes TOSense-a Chrome e…
Instance-Wise Monotonic Calibration by Constrained Transformation
Yunrui Zhang, Gustavo Batista, Salil S. Kanhere
Deep neural networks often produce miscalibrated probability estimates, leading to overconfident predictions. A common approach for calibration is fitting a post-hoc calibration ma…
What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation?
Erik Buchholz, Natasha Fernandes, David D. Nguyen +3
While location trajectories offer valuable insights, they also reveal sensitive personal information. Differential Privacy (DP) offers formal protection, but achieving a favourable…