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
    Formally Compensating Performance Limitations for Imprecise 2D Object Detection
    ( 2022-08-25)
    Schuster, Tobias
    ;
    Seferis, Emmanouil
    ;
    Burton, Simon
    ;
    Cheng, Chih-Hong
    In this paper, we consider the imperfection within machine learning-based 2D object detection and its impact on safety. We address a special sub-type of performance limitations related to the misalignment of bounding-box predictions to the ground truth: the prediction bounding box cannot be perfectly aligned with the ground truth. We formally prove the minimum required bounding box enlargement factor to cover the ground truth. We then demonstrate that this factor can be mathematically adjusted to a smaller value, provided that the motion planner uses a fixed-length buffer in making its decisions. Finally, observing the difference between an empirically measured enlargement factor and our formally derived worst-case enlargement factor offers an interesting connection between quantitative evidence (demonstrated by statistics) and qualitative evidence (demonstrated by worst-case analysis) when arguing safety-relevant properties of machine learning functions.