The field of image generation and processing is rapidly evolving, driven by the need for authentic and integrity-assured visual content. A common theme among recent developments is the use of diffusion models and innovative techniques for image synthesis, editing, and enhancement. Researchers are exploring methods to verify the origin and legitimacy of images, addressing concerns around deepfakes, copyright infringement, and data privacy. Notable advances include the development of robust watermarking techniques, auditing frameworks for data provenance, and forensic tools for identifying the source of manipulated images. These innovations have significant implications for various applications, including digital security, media integrity, and cultural preservation. Recent studies have also focused on improving the efficiency and accuracy of diffusion models, particularly in handling complex data distributions and inverse problems. The introduction of novel frameworks and the extension of existing methods have shown promising results in addressing challenging tasks. Furthermore, the exploration of diffusion models in various applications, including image restoration and optimization problems, has led to innovative solutions and improved performance. The use of diffusion models and generative adversarial networks is also becoming increasingly popular for tasks such as image relighting and video super-resolution. Overall, the field is moving towards more sophisticated and controllable image synthesis and editing methods, with a focus on preserving causal relationships and achieving high-quality results. Noteworthy papers in this area include proposals for novel distortion-free watermarking methods, dual-domain approaches to watermarking diffusion models, and lightweight frameworks for attributing face-swap deepfakes to their source models.