3 papers
cs.LG2024
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…
cs.CV2024
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.…
cs.CV2023
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…