Image forgery detection and localization （IFDL） determines whether an image has been manipulated or synthesized in its entirety and identifies locally manipulated regions. Generative editing and whole-image synthesis change the available forensic traces and broaden the scope of detection research. This survey reviews representative literature from 2005 to 2026. Conventional local manipulation， generative local editing and fully synthetic images are distinguished by how their pixels are produced. Their traces， datasets and evaluation metrics are summarized. Representative methods are then discussed along three research directions： conventional forensic cues， generative forgery analysis and multimodal large language models. The review compares their mechanisms， applicable conditions， evaluation settings and performance under transmission degradation. The analysis shows that image-level labels and pixel-level masks support different evaluation tasks， while the reported composition of genuine and forged images is inconsistent across publications for some datasets. Training data， supervision， input resolution and threshold selection affect performance comparisons； some differences in evaluation settings also change method rankings. Under generative editing， classical cues depend on background preservation， region blending and subsequent processing. Generator artifacts， diffusion-inversion residuals and pretrained features provide complementary evidence， but generalization remains sensitive to differences between generators and data distributions. Multimodal large language models introduce natural-language explanations， yet methods differ in their support for localization masks and source attribution. Explanation readability and faithfulness to visual evidence require separate assessment. The effects of compression， resizing and noise are related to the cues used； controlled experiments are still needed to distinguish the contributions of training data and network architecture. This survey provides a reference for selecting forensic evidence， designing comparable evaluations and choosing methods for practical deployment.