Digital photography is a powerful medium for capturing and conveying specific moments in space and time, having the power to shape our understanding of the world and influence our actions. Consequently, altering the semantics of an image through manipulation can distort our worldview and judgment. This creates the need for tools to verify the integrity of visual content. From the earliest days of digital photography, image editing and tampering techniques have significantly evolved, culminating in today’s generative artificial intelligence (AI) tools. Given the variety and diversity of image forgery approaches, it is no wonder that a diverse set of countering techniques have been developed for their detection. In this chapter, we outline the recent advances on detecting image forgery and how generative AI fundamentally reshapes the field. We study the foundations of image forensics and the employment of deep learning architectures to craft robust detectors. We present recently introduced benchmarks that capture the challenges imposed by generative AI and we explore the use of data-driven approaches as means to increase robustness for images of unknown origin, such as the ones sourced from the Web. Overall, this chapter aims to highlight the capabilities of today’s image forensics approaches and identify the open questions likely to shape future research in the field.