Dr. rer. nat.

Cheng, Chih-Hong

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  • Publication
    Butterfly Effect Attack: Tiny and Seemingly Unrelated Perturbations for Object Detection
    ( 2023)
    Doan, Nguyen Anh Vu
    ;
    Yüksel, Arda
    ;
    Cheng, Chih-Hong
    This work aims to explore and identify tiny and seemingly unrelated perturbations of images in object detection that will lead to performance degradation. While tininess can naturally be defined using Lp norms, we characterize the degree of "unrelatedness" of an object by the pixel distance between the occurred perturbation and the object. Triggering errors in prediction while satisfying two objectives can be formulated as a multi-objective optimization problem where we utilize genetic algorithms to guide the search. The result successfully demonstrates that (invisible) perturbations on the right part of the image can drastically change the outcome of object detection on the left. An extensive evaluation reaffirms our conjecture that transformer-based object detection networks are more susceptible to butterfly effects in comparison to single-stage object detection networks such as YOLOv5.
  • Publication
    Potential-based Credit Assignment for Cooperative RL-based Testing of Autonomous Vehicles
    ( 2023)
    Ayvaz, Utku
    ;
    Cheng, Chih-Hong
    ;
    Hao, Shen
    While autonomous vehicles (AVs) may perform remarkably well in generic real-life cases, their irrational action in some unforeseen cases leads to critical safety concerns. This paper introduces the concept of collaborative reinforcement learning (RL) to generate challenging test cases for AV planning and decision-making module. One of the critical challenges for collaborative RL is the credit assignment problem, where a proper assignment of rewards to multiple agents interacting in the traffic scenario, considering all parameters and timing, turns out to be non-trivial. In order to address this challenge, we propose a novel potential-based reward-shaping approach inspired by counterfactual analysis for solving the credit-assignment problem. The evaluation in a simulated environment demonstrates the superiority of our proposed approach against other methods using local and global rewards.
  • Publication
    Safeguarding Learning-based Control for Smart Energy Systems with Sampling Specifications
    ( 2023)
    Cheng, Chih-Hong
    ;
    Gupta, Pragya Kirti
    ;
    Venkataramanan, Venkatesh Prasad
    ;
    Hsu, Yun-Fei
    ;
    Burton, Simon
    We study challenges using reinforcement learning in controlling energy systems, where apart from performance requirements, one has additional safety requirements such as avoiding blackouts. We detail how these safety requirements in real-time temporal logic can be strengthened via discretization into linear temporal logic (LTL), such that the satisfaction of the LTL formulae implies the satisfaction of the original safety requirements. The discretization enables advanced engineering methods such as synthesizing shields for safe reinforcement learning as well as formal verification, where for statistical model checking, the probabilistic guarantee acquired by LTL model checking forms a lower bound for the satisfaction of the original real-time safety requirements.