4 papers
Data-Driven Lipschitz Continuity: A Cost-Effective Approach to Improve Adversarial Robustness
Erh-Chung Chen, Pin-Yu Chen, I-Hsin Chung +1
As deep neural networks (DNNs) are increasingly deployed in sensitive applications, ensuring their security and robustness has become critical. A major threat to DNNs arises from a…
LTD: Low Temperature Distillation for Gradient Masking-free Adversarial Training
Erh-Chung Chen, Che-Rung Lee
Adversarial training is a widely adopted strategy to bolster the robustness of neural network models against adversarial attacks. This paper revisits the fundamental assumptions un…
Overload: Latency Attacks on Object Detection for Edge Devices
Erh-Chung Chen, Pin-Yu Chen, I-Hsin Chung +1
Nowadays, the deployment of deep learning-based applications is an essential task owing to the increasing demands on intelligent services. In this paper, we investigate latency att…
Steal Now and Attack Later: Evaluating Robustness of Object Detection against Black-box Adversarial Attacks
Erh-Chung Chen, Pin-Yu Chen, I-Hsin Chung +1
Latency attacks against object detection represent a variant of adversarial attacks that aim to inflate the inference time by generating additional ghost objects in a target image.…