Trident: Detecting Face Forgeries with Adversarial Triplet Learning

• URL: http://arxiv.org/abs/2506.23189v1
• Date: Sun, 29 Jun 2025 11:17:25 GMT
• Title: Trident: Detecting Face Forgeries with Adversarial Triplet Learning
• Authors: Mustafa Hakan Kara, Aysegul Dundar, Uğur Güdükbay,
• Abstract summary: textitTrident is trained on curated triplets to isolate nuanced differences of forgeries.<n> domain-adversarial training with a forgery discriminator guides our embedding model towards forgery-agnostic representations.
• Score: 5.101710685818588
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As face forgeries generated by deep neural networks become increasingly sophisticated, detecting face manipulations in digital media has posed a significant challenge, underscoring the importance of maintaining digital media integrity and combating visual disinformation. Current detection models, predominantly based on supervised training with domain-specific data, often falter against forgeries generated by unencountered techniques. In response to this challenge, we introduce \textit{Trident}, a face forgery detection framework that employs triplet learning with a Siamese network architecture for enhanced adaptability across diverse forgery methods. \textit{Trident} is trained on curated triplets to isolate nuanced differences of forgeries, capturing fine-grained features that distinguish pristine samples from manipulated ones while controlling for other variables. To further enhance generalizability, we incorporate domain-adversarial training with a forgery discriminator. This adversarial component guides our embedding model towards forgery-agnostic representations, improving its robustness to unseen manipulations. In addition, we prevent gradient flow from the classifier head to the embedding model, avoiding overfitting induced by artifacts peculiar to certain forgeries. Comprehensive evaluations across multiple benchmarks and ablation studies demonstrate the effectiveness of our framework. We will release our code in a GitHub repository.

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