AI-assisted deepfake detection using adaptive blind image watermarking

• Published in: Journal of visual communication and image representation Vol. 100; p. 104094
• Main Author: Hsu, Ling-Yuan
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.04.2024
• Subjects: Blind image watermarking; Deepfake detection; Mixed modulation; Partly sign-altered; Mixed modulation; Deepfake detection; Partly sign-altered; Blind image watermarking
• ISSN: 1047-3203; 1095-9076
• DOI: 10.1016/j.jvcir.2024.104094

•A new adaptive blind image watermarking technology, utilizing artificial intelligence (AI) and named AwDD, has been proposed for detecting color image deepfakes.•The AI technology used includes face detection, denoising autoencoder (DAE), and the gray wolf optimizer (GWO).•The proposed method employs mixed modulation (MM) with partly sign-altered mean value (PSAMV) to embed a set of coefficients, thereby increasing robustness against attacks while maintaining high image quality.•Blind adaptive deepfake detection (BADD) with the tamper detection mean value (TDMV) is applied to adaptively detect changes in the relative position when a face image is modified or deepfaked.•The GWO optimizes parameters to enhance imperceptibility and robustness, while the DAE improves the visual recognizability of the extracted watermark. This paper proposes a new adaptive blind watermarking technology for deepfake detection, which can embed deepfake detection information into the image and verify the image's authenticity without requiring additional information. The proposed scheme utilizes mixed modulation combined with partly sign-altered mean value to embed a set of coefficients that enhance robustness against attacks while maintaining high image quality. Additionally, blind adaptive deepfake detection technology with the tamper detection mean value is employed to detect relative positions adaptively, even when face images are slightly modified or deepfaked. To further improve the performance of the proposed scheme, a gray wolf optimizer is introduced to optimize parameters, and a denoising autoencoder is employed to facilitate the identification of extracted watermarks. This technology will adaptively embed watermark information while preserving the original face image, thereby maintaining the authenticity of the face in the image and verifying the owner of the image. The code is available at https://github.com/lyhsu01/AwDD.