LAUSR

DeepFake Detection for Human Face Images and Videos: A Survey

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2 Published in 2022 at "IEEE Access"

DOI: 10.1109/access.2022.3151186

Abstract: Techniques for creating and manipulating multimedia information have progressed to the point where they can now ensure a high degree of realism. DeepFake is a generative deep learning algorithm that creates or modifies face features… read more here.

Keywords: images videos; survey; detection; face images ... See more keywords

Deepfake Detection: A Systematic Literature Review

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1 Published in 2022 at "IEEE Access"

DOI: 10.1109/access.2022.3154404

Abstract: Over the last few decades, rapid progress in AI, machine learning, and deep learning has resulted in new techniques and various tools for manipulating multimedia. Though the technology has been mostly used in legitimate applications… read more here.

Keywords: systematic literature; detection; literature review; deepfake detection ... See more keywords

Generalization of Forgery Detection With Meta Deepfake Detection Model

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2 Published in 2023 at "IEEE Access"

DOI: 10.1109/access.2022.3232290

Abstract: Face forgery generating algorithms that produce a range of manipulated videos/images have developed quickly. Consequently, this causes an increase in the production of fake information, making it difficult to identify. Because facial manipulation technologies raise… read more here.

Keywords: meta deepfake; forgery detection; detection; model ... See more keywords

Transformer-Based Feature Compensation and Aggregation for DeepFake Detection

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1 Published in 2022 at "IEEE Signal Processing Letters"

DOI: 10.1109/lsp.2022.3214768

Abstract: Deepfake detection has attracted increasing attention in recent years. In this paper, we propose a transformer-based framework with feature compensation and aggregation (Trans-FCA) to extract rich forgery cues for deepfake detection. To compensate local features… read more here.

Keywords: transformer based; detection; compensation aggregation; deepfake detection ... See more keywords

Learning Features of Intra-Consistency and Inter-Diversity: Keys Toward Generalizable Deepfake Detection

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2 Published in 2023 at "IEEE Transactions on Circuits and Systems for Video Technology"

DOI: 10.1109/tcsvt.2022.3209336

Abstract: Public concerns about deepfake face forgery are continually rising in recent years. Most deepfake detection approaches attempt to learn discriminative features between real and fake faces through end-to-end trained deep neural networks. However, the majorities… read more here.

Keywords: consistency; diversity; deepfake; deepfake detection ... See more keywords

Artifacts-Disentangled Adversarial Learning for Deepfake Detection

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2 Published in 2023 at "IEEE Transactions on Circuits and Systems for Video Technology"

DOI: 10.1109/tcsvt.2022.3217950

Abstract: Due to the development of facial manipulation technologies, the generated deepfake videos cause a severe trust crisis in society. Existing methods prove that effective extraction of the artifacts introduced during the forgery process is essential… read more here.

Keywords: artifacts disentangled; detection; adversarial learning; disentangled adversarial ... See more keywords

ISTVT: Interpretable Spatial-Temporal Video Transformer for Deepfake Detection

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2 Published in 2023 at "IEEE Transactions on Information Forensics and Security"

DOI: 10.1109/tifs.2023.3239223

Abstract: With the rapid development of Deepfake synthesis technology, our information security and personal privacy have been severely threatened in recent years. To achieve a robust Deepfake detection, researchers attempt to exploit the joint spatial-temporal information… read more here.

Keywords: interpretable spatial; temporal video; video transformer; spatial temporal ... See more keywords

Masked Relation Learning for DeepFake Detection

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2 Published in 2023 at "IEEE Transactions on Information Forensics and Security"

DOI: 10.1109/tifs.2023.3249566

Abstract: DeepFake detection aims to differentiate falsified faces from real ones. Most approaches formulate it as a binary classification problem by solely mining the local artifacts and inconsistencies of face forgery, which neglect the relation across… read more here.

Keywords: masked relation; relation learning; learning deepfake; deepfake detection ... See more keywords