Generalizable Face Forgery Detection with Metric Learning and Domain-Adversarial Training
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| Publicado en: | PQDT - Global (2025) |
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ProQuest Dissertations & Theses
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 001 | 3235007799 | ||
| 003 | UK-CbPIL | ||
| 020 | |a 9798290652306 | ||
| 035 | |a 3235007799 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 189128 |2 nlm | ||
| 100 | 1 | |a Kara, Mustafa Hakan | |
| 245 | 1 | |a Generalizable Face Forgery Detection with Metric Learning and Domain-Adversarial Training | |
| 260 | |b ProQuest Dissertations & Theses |c 2025 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a 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 Trident,a face forgery detection framework that employs triplet learning with a Siamese network architecture for enhanced adaptability across diverse forgery methods. Tridentis 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. | |
| 653 | |a Physiology | ||
| 653 | |a Forgery | ||
| 653 | |a Deep learning | ||
| 653 | |a Deepfake | ||
| 653 | |a Back propagation | ||
| 653 | |a Video recordings | ||
| 653 | |a Neural networks | ||
| 653 | |a Adaptation | ||
| 653 | |a Design | ||
| 653 | |a Semantics | ||
| 653 | |a Triplets | ||
| 773 | 0 | |t PQDT - Global |g (2025) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3235007799/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3235007799/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |