Exploring Active Data Selection Strategies for Continuous Training in Deepfake Detection
In deepfake detection, it is essential to maintain high performance by adjusting the parameters of the detector as new deepfake methods emerge. In this paper, we propose a method to automatically and actively select the small amount of additional data required for the continuous training of deepfake detection models in situations where deepfake detection models are regularly updated. The proposed method automatically selects new training data from a \textit{redundant} pool set containing a large number of images generated by new deepfake methods and real images, using the confidence score of the deepfake detection model as a metric. Experimental results show that the deepfake detection model, continuously trained with a small amount of additional data automatically selected and added to the original training set, significantly and efficiently improved the detection performance, achieving an EER of 2.5% with only 15% of the amount of data in the pool set.
Code (0)
등록된 구현이 없습니다.
Tasks
DeepFake DetectionFace SwappingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Evaluation of Seed Set Selection Approaches and Active Learning Strategies in Predictive Coding
Active learning is a popular methodology in text classification - known in the legal domain as "predictive coding" or "Technology Assisted Review" or "TAR" - due to its potential to minimize the required review effort to…
Active LearningClusteringTARtext-classification+1Active Learning for Natural Language Generation
The field of Natural Language Generation (NLG) suffers from a severe shortage of labeled data due to the extremely expensive and time-consuming process involved in manual annotation. A natural approach for coping with th…
Active LearningClassificationtext-classificationText Classification+1Exploring Active Learning for Label-Efficient Training of Semantic Neural Radiance Field
Neural Radiance Field (NeRF) models are implicit neural scene representation methods that offer unprecedented capabilities in novel view synthesis. Semantically-aware NeRFs not only capture the shape and radiance of a sc…
Novel View SynthesisActive LearningAutoFS: Automated Feature Selection via Diversity-aware Interactive Reinforcement Learning
In this paper, we study the problem of balancing effectiveness and efficiency in automated feature selection. Feature selection is a fundamental intelligence for machine learning and predictive analysis. After exploring …
Diversityfeature selectionNavigatereinforcement-learning+2Expected Free Energy-based Informative Path Planning for Robotic Mars Exploration
An autonomous robot efficiently exploring an unknown environment, such as looking for water sources on Mars, faces two simultaneous demands: building an accurate information map while quickly finding the regions of great…