Core Viewpoint - The article discusses the development of FractalForensics, a novel method for active deepfake detection and localization using fractal watermarking, addressing existing challenges in deepfake detection and positioning [4][5][12]. Group 1: Introduction and Motivation - Recent years have seen a growing interest in active defenses against deepfakes, with existing methods like robust and semi-fragile watermarks showing limited effectiveness [4]. - The paper aims to tackle the issues of existing watermarking techniques, which struggle with robustness and the simultaneous detection and localization of forgeries [8]. Group 2: Methodology - FractalForensics introduces a watermarking approach that utilizes a matrix format instead of traditional watermark vectors, enhancing the capability for forgery localization [5]. - The watermark generation and encryption process is parameterized, allowing users to select values for various parameters, resulting in 144 different fractal variants [6][9]. - A chaotic encryption system is constructed based on fractal geometry, which enhances the security and variability of the watermark [7]. Group 3: Watermark Embedding and Extraction - The watermark embedding model is based on convolutional neural networks, employing an entry-to-patch strategy to embed watermarks into images without disrupting their integrity [10][11]. - The method ensures that modified areas in deepfake images lose the watermark, enabling both detection and localization of forgeries [11][18]. Group 4: Experimental Results - The proposed watermarking method demonstrates optimal robustness against common image processing techniques, maintaining high detection rates [13][14]. - In tests against various deepfake methods, FractalForensics shows reasonable vulnerability, allowing for effective detection and localization [15][16]. - The article presents comparative results indicating that FractalForensics achieves superior detection performance compared to state-of-the-art passive detection methods [17][18].