With the advancement of technology, the alteration of photographs has become more complex, presenting significant difficulties in areas such as security, journalism, and digital forensics. It is crucial for guaranteeing the genuineness and uncorrupted nature of visual material. This review presents a thorough overview of the current methodologies employed in the detection of forgery in digital images. The classification includes pixel-based, format-based, camera-based, geometry-based, deep learning-based, hybrid, metadata- and physics-based, digital signature and water-marketing methods. Pixel-based identifies irregularities at the individual pixel level. Format-based methods utilize compression artifacts, specifically those related to JPEG. Camera-based techniques employ unique sensor patterns and artifacts introduced during the image-capturing process. Geometry-based methods investigate discrepancies in spatial configuration and physical characteristics. Deep learning approaches use sophisticated neural networks to detect subtle indications of tampering. Physics-based methods examine the physical properties of the images. Digital watermarking involves the discreet embedding of secret information. A digital signature is a cryptographic technique that guarantees the image’s authenticity and unaltered state. This evaluation examines the advantages and drawbacks of each approach, offering valuable insights into their practical uses and efficacy. In addition, we analyze the difficulties of identifying digital picture forgeries, highlighting the necessity for stronger and more flexible methods to combat ever-more advanced forgeries.