TI 2 Net: Temporal Identity Inconsistency Network for Deepfake Detection
Abstract
In this paper, we propose the Temporal Identity Inconsistency Network (TI2Net), a Deepfake detector that focuses on temporal identity inconsistency. Specifically, TI2Net recognizes fake videos by capturing the dissimilarities of human faces among video frames of the same identity. Therefore, TI2Net is a reference-agnostic detector and can be used on unseen datasets. For a video clip of a given identity, identity information in all frames will first be encoded to identity vectors. TI2Net learns the temporal identity embedding from the temporal difference of the identity vectors. The temporal embedding, representing the identity inconsistency in the video clip, is finally used to determine the authenticity of the video clip. During training, TI2Net incorporates triplet loss to learn more discriminative temporal embeddings. We conduct comprehensive experiments to evaluate the performance of the proposed TI2Net. Experimental results indicate that TI2Net generalizes well to unseen manipulations and datasets with unseen identities. Besides, TI2Net also shows robust performance against compression and additive noise.
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Image-based detectors that achieve near-perfect accuracy cannot simply be applied frame-by-frame to videos, because they miss critical temporal inconsistencies such as unnatural head movements, flickering artifacts, and inter-frame coherence violations.
“Image-based detectors that achieve near-perfect accuracy cannot simply be applied frame-by-frame to videos, as they miss critical temporal inconsistencies such as unnatural head movements, flickering artifacts, and inter-frame coherence violations [10-12].”
From Deepfake detection across image, video, and audio: a comprehensive survey with empirical evaluation of generalization and robustness (2026) · cites this paper as [10]
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- Authors
- Liu, B., Liu, B., Ding, M., Zhu, T., & Yu, X.
- Affiliations
- University of Technology Sydney (AU)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Venue
- 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
- Publisher
- IEEE
- Published
- 2023-01
- Pages
- 4680-4689
- ISSN
- 2472-6737 (ISSN-L)
- OpenAlex
- W4319300180
- Cited by
- 50
- References
- 27
- Citations by year
- 2026: 11 · 2025: 18 · 2024: 17 · 2023: 4
- FWCI
- 5.35
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Winter Conference on Applications of Computer Vision (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- deepfake detection, video authenticity, identity embedding, triplet loss, temporal embeddings
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