Donghee Hong

Donghee Hong, Minjong Kim, Sooyoung Cha et al.

Symbolic execution engines such as KLEE automatically generate test cases to maximize branch coverage, but their numerous parameters make it difficult to understand the parameters' impact, leading the user to rely on suboptimal default configurations. While automated tuners have shown promising results, they provide limited insights into why certain configurations work well, motivating the need for Human-in-the-Loop approaches. In this work, we present a visual analytics system, Symetra, designed to support Human-in-the-Loop parameter tuning of symbolic execution engines. To handle a large number of parameters and their configurations, we provide two complementary overviews of their impact on branch coverage values and patterns. Building on these overviews, our system enables collective analysis, allowing the user to contrast groups of configurations and identify differences that may affect branch coverage. We also report on case studies and a Human-in-the-Loop tuning process, demonstrating that experts not only interpreted parameter impacts and identified complementary configurations, but also improved upon fully automated approaches in both branch coverage and tuning efficiency.

Donggeun Ko, Sangjun Lee, Jinyong Park et al.

Hyper-realistic face image generation and manipulation have givenrise to numerous unethical social issues, e.g., invasion of privacy,threat of security, and malicious political maneuvering, which re-sulted in the development of recent deepfake detection methods with the rising demands of deepfake forensics. Proposed deepfake detection methods to date have shown remarkable detection performance and robustness. However, none of the suggested deepfake detection methods assessed the performance of deepfakes with the facemask during the pandemic crisis after the outbreak of theCovid-19. In this paper, we thoroughly evaluate the performance of state-of-the-art deepfake detection models on the deepfakes with the facemask. Also, we propose two approaches to enhance the masked deepfakes detection: face-patch and face-crop. The experimental evaluations on both methods are assessed through the base-line deepfake detection models on the various deepfake datasets. Our extensive experiments show that, among the two methods, face-crop performs better than the face-patch, and could be a train method for deepfake detection models to detect fake faces with facemask in real world.