2026 International Conference on Networks Computers and Communications (ICNCCom 2026)

Digital forensic systems rely on the secure exchange of image data, where maintaining integrity and authenticity is essential for reliable decision-making. However, the ease of manipulating and replicating digital images introduces serious security challenges during storage and transmission. This paper proposes a method for securing forensic images by combining watermarking techniques with deep learning approaches. The proposed method aims to preserve the original content while embedding authentication information that enables verification without affecting the evidence. In addition, the study evaluates the performance of the method based on key factors such as robustness, image quality, and security level. The results demonstrate that the proposed approach provides a balanced trade-off between protection and fidelity, making it suitable for forensic applications that require high levels of trust and reliability.

integrity,authenticity,deep learning,Digital forensic systems,watermarking,verification