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With the availability of easy-to-use image editing tools, the generation of altered images has become an effortless task. Apart from this, advancements in the Generative Adversarial Network (GAN)\u00a0leads to creation of realistic facial images and alteration of facial images based on the attributes. While the majority of these images are created for fun and beautification purposes, they may be used with malicious intent for negative applications such as deepnude or spreading visual fake news. Therefore, it is important to detect digital alterations in images and videos. This chapter presents a comprehensive survey of existing algorithms for retouched and altered image detection. Further, multiple experiments are performed to highlight the open challenges of alteration detection.<\/jats:p>","DOI":"10.1007\/978-3-030-87664-7_17","type":"book-chapter","created":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T09:03:06Z","timestamp":1643619786000},"page":"367-387","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Facial Retouching and Alteration Detection"],"prefix":"10.1007","author":[{"given":"Puspita","family":"Majumdar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akshay","family":"Agarwal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mayank","family":"Vatsa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richa","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,31]]},"reference":[{"key":"17_CR1","unstructured":"68 percent of adults edit their selfies before sharing them with anyone. https:\/\/fstoppers.com\/mobile\/68-percent-adults-edit-their-selfies-sharing-them-anyone-95417. 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