{"type":"ScholarlyArticle","title":"A Fourier Perspective of Feature Extraction and Adversarial Robustness","authors":[{"name":"Liangqi Zhang","slug":"liangqi-zhang","given":"Liangqi","family":"Zhang","affiliation":"Huazhong University of Science and Technology"},{"name":"Yihao Luo","slug":"yihao-luo","given":"Yihao","family":"Luo","affiliation":"Yichang Testing Technique R&D Institute"},{"name":"Haibo Shen","slug":"haibo-shen","given":"Haibo","family":"Shen","affiliation":"Huazhong University of Science and Technology"},{"name":"Tianjiang Wang","slug":"tianjiang-wang","given":"Tianjiang","family":"Wang","affiliation":"Huazhong University of Science and Technology"}],"abstract":"Adversarial robustness and interpretability are longstanding challenges of computer vision. Deep neural networks are vulnerable to adversarial perturbations that are incomprehensible and imperceptible to humans. However, the opaqueness of networks prevents one from theoretically addressing adversarial robustness. As a human-comprehensible approach, the frequency perspective has been adopted in recent works to investigate the properties of neural networks and adversarial examples.\r\nIn this paper, we investigate the frequency properties of feature extraction and analyze the stability of different frequency features when attacking different frequencies. Therefore, we propose an attack method, F-PGD, based on the projected gradient descent to attack the specified frequency bands. Utilizing this method, we find many intriguing properties of neural networks and adversarial perturbations. We experimentally show that contrary to the low-frequency bias of neural networks, the effective features of the same class are distributed across all frequency bands. Meanwhile, the high-frequency features often dominate when the neural networks make conflicting decisions on different frequency features.\r\nFurthermore, the attack experiments show that the low-frequency features are more robust to the attacks on different frequencies, but the interference to the high frequencies makes the network unable to make the right decision. These properties indicate that the decision-making process of neural networks tends to use as few low-frequency features as possible and cannot integrate features of different frequencies.","doi":"10.24963/ijcai.2024/190","url":"https://ijcai.org/proceedings/2024/190","pdf":"https://ijcai.org/proceedings/2024/0190.pdf","markdown":"https://ijcai.org/proceedings/2024/190.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/190","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":190,"pages":{"first":1715,"last":1723},"keywords":["Computer Vision: CV: Recognition (object detection, categorization)","Computer Vision: CV: Adversarial learning, adversarial attack and defense methods","Computer Vision: CV: Interpretability and transparency"],"videos":[],"references":[{"position":1,"raw":"[Brendel and Bethge, 2019] Wieland Brendel and Matthias Bethge. 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