Researchers at Queen’s University in Canada have explored watermarking as a method to tag AI images for verification of origin and integrity. Watermarking systems operate as a complete security process, consisting of embedding, verification, attack channels, and detection. The watermark must be invisible to viewers but readable to authorized users, and remain private enough that no one can duplicate it. Watermarking began with signal-processing methods that changed pixel values or frequency coefficients using transforms. The rise of deep learning introduced new possibilities, such as encoder-decoder networks and diffusion models. Researchers began embedding marks directly inside these systems, producing two main approaches: fine-tuning-based and initial noise-based. Visual quality, capacity, and detectability are the main criteria for evaluating watermarking. Researchers use metrics such as Structural Similarity Index (SSIM) and Fréchet Inception Distance (FID) to check that the mark does not degrade the picture. Capacity measures how much data can be stored, while detectability refers to how reliably a watermark can be recovered after changes or attacks. Watermarking schemes remain fragile under pressure, with threats dividing into resilience and security. New attack strategies take advantage of diffusion models, such as regeneration attacks and detector-aware attacks. Defensive ideas include encrypting watermark keys, varying where marks are placed, and training models to recognize and preserve watermarks during generation. Global momentum is building for watermarked AI content, with governments and private companies experimenting with watermarking methods.