{"type":"ScholarlyArticle","title":"Unlocking the Potential of Lightweight Quantized Models for Deepfake Detection","authors":[{"name":"Renshuai Tao","slug":"renshuai-tao","given":"Renshuai","family":"Tao","affiliation":""},{"name":"Ziheng Qin","slug":"ziheng-qin","given":"Ziheng","family":"Qin","affiliation":""},{"name":"Yifu Ding","slug":"yifu-ding","given":"Yifu","family":"Ding","affiliation":""},{"name":"Chuangchuang Tan","slug":"chuangchuang-tan","given":"Chuangchuang","family":"Tan","affiliation":""},{"name":"Jiakai Wang","slug":"jiakai-wang","given":"Jiakai","family":"Wang","affiliation":""},{"name":"Wei Wang","slug":"wei-wang","given":"Wei","family":"Wang","affiliation":""}],"abstract":"Deepfake detection is increasingly crucial due to the rapid rise of AI-generated content. Existing methods achieve high performance relying on computationally intensive large models, making real-time detection on resource-constrained edge devices challenging. Given that deepfake detection is a binary classification task, there is potential for model compression and acceleration. In this paper, we propose a low-bit quantization framework for lightweight and efficient deepfake detection. The Connected Quantized Block extracts common forgery features via the quantized path and retains method-specific textures through the shortcut connections. Additionally, the Shifted Logarithmic Redistribution Quantizer mitigates information loss in near-zero domains by unfolding the unbalanced activations, enabling finer quantization granularity. Comprehensive experiments demonstrate that this new framework significantly reduces 10.8x computational costs and 12.4x storage requirements while maintaining high detection performance, even surpassing SOTA methods using less than 5% FLOPs, paving the way for efficient deepfake detection in resource-limited scenarios.","doi":"10.24963/ijcai.2025/59","url":"https://ijcai.org/proceedings/2025/59","pdf":"https://ijcai.org/proceedings/2025/0059.pdf","markdown":"https://ijcai.org/proceedings/2025/59.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/59","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":"AI Ethics, Trust, Fairness","track":"Main Track","number":59,"pages":{"first":520,"last":528},"keywords":["AI Ethics, Trust, Fairness: ETF: Trustworthy AI","AI Ethics, Trust, Fairness: ETF: Safety and robustness"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}