{"type":"ScholarlyArticle","title":"RepObE: Representation Learning-Enhanced Obfuscation Encryption Modular Semantic Task Framework","authors":[{"name":"Limei Lin","slug":"limei-lin","given":"Limei","family":"Lin","affiliation":""},{"name":"Jinpeng Xu","slug":"jinpeng-xu","given":"Jinpeng","family":"Xu","affiliation":""},{"name":"Xiaoding Wang","slug":"xiaoding-wang","given":"Xiaoding","family":"Wang","affiliation":""},{"name":"Liang Chen","slug":"liang-chen","given":"Liang","family":"Chen","affiliation":""},{"name":"Sun-Yuan Hsieh","slug":"sun-yuan-hsieh","given":"Sun-Yuan","family":"Hsieh","affiliation":""},{"name":"Jie Wu","slug":"jie-wu","given":"Jie","family":"Wu","affiliation":""}],"abstract":"Model inversion and adversarial attacks in semantic communication pose risks, such as content leaks, alterations, and prediction inaccuracies, which threaten security and reliability. This paper introduces, from an attacker's viewpoint, a novel framework called RepObE (Representation Learning-Enhanced Obfuscation Encryption Modular Semantic Task Framework) to secure semantic communication. This framework employs dynamic encryption during semantic extraction and feature transmission to hinder attackers from reconstructing data through eavesdropping, thus strengthening system privacy. To combat image communication task challenges, we propose a prototype adversarial collaborative alignment training approach enhanced by representation learning. This method extracts and encodes semantic features while using dynamic perturbation and robust optimization to improve system resilience against adversarial threats. The approach ensures reliable semantic communication in complex environments, maintaining performance while countering attacks using feature obfuscation, adversarial training, and representation learning. Experimental results demonstrate that our method surpasses existing techniques by more than 2% in resisting model inversion attacks on classification tasks. Visually, our method excels with minimal decipherable images for attackers. It also shows a 3% to 5% improvement in countering adversarial attacks on classification tasks.","doi":"10.24963/ijcai.2025/635","url":"https://ijcai.org/proceedings/2025/635","pdf":"https://ijcai.org/proceedings/2025/0635.pdf","markdown":"https://ijcai.org/proceedings/2025/635.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/635","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Machine Learning","track":"Main Track","number":635,"pages":{"first":5707,"last":5715},"keywords":["AI Ethics, Trust, Fairness: ETF: Explainability and interpretability","Machine Learning: ML: Representation learning","Multidisciplinary Topics and Applications: MTA: Ubiquitous computing cystems","Multidisciplinary Topics and Applications: MTA: Web and social networks"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}