2021 IEEE International Conference on Image Processing
19-22 September 2021 • Anchorage, Alaska, USA
Imaging Without Borders
| Paper ID | SS-MMSDF-1.9 | ||
| Paper Title | RoBIC: A BENCHMARK SUITE FOR ASSESSING CLASSIFIERS ROBUSTNESS | ||
| Authors | Thibault Maho, Benoit Bonnet, Teddy Furon, Erwan Le Merrer, Inria, France | ||
| Session | SS-MMSDF-1: Special Session: AI for Multimedia Security and Deepfake 1 | ||
| Location | Area B | ||
| Session Time: | Monday, 20 September, 15:30 - 17:00 | ||
| Presentation Time: | Monday, 20 September, 15:30 - 17:00 | ||
| Presentation | Poster | ||
| Topic | Special Sessions: Artificial Intelligence for Multimedia Security and Deepfake | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | Many defenses have emerged with the development of adversarial attacks. Models must be objectively evaluated accordingly. This paper systematically tackles this concern by proposing a new parameter-free benchmark we coin RoBIC. RoBIC fairly evaluates the robustness of image classifiers using a new half-distortion measure. It gauges the robustness of the network against white and black box attacks, independently of its accuracy. RoBIC is faster than the other available benchmarks. We present the significant differences in the robustness of 16 recent models as assessed by RoBIC. We make this benchmark publicly available for use and contribution at https://gitlab.inria.fr/tmaho/robustness_benchmark. | ||