{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T03:03:38Z","timestamp":1771038218609,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T00:00:00Z","timestamp":1722902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Extensive research has been conducted in image forensics on the analysis of double-compressed images, particularly in the widely adopted JPEG format. However, there is a lack of methods to detect double compression in the HEIF format, which has recently gained popularity since it allows for reduced file size while maintaining image quality. Traditional JPEG-based techniques do not apply to HEIF due to its distinct encoding algorithms. We previously proposed a method to detect double compression in HEIF images based on Farid\u2019s work on coding ghosts in JPEG images. However, this method was limited to scenarios where the quality parameter used for the first encoding was larger than for the second encoding. In this study, we propose a lightweight image classifier to extend the existing model, enabling the identification of double-compressed images without heavily depending on the input image\u2019s quantization history. This extended model outperforms the previous approach and, despite its lightness, demonstrates excellent detection accuracy.<\/jats:p>","DOI":"10.3390\/s24165103","type":"journal-article","created":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T15:24:16Z","timestamp":1722957856000},"page":"5103","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Lightweight Double Compression Detector for HEIF Images Based on Encoding Information"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5672-3820","authenticated-orcid":false,"given":"Yoshihisa","family":"Furushita","sequence":"first","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Florence, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0630-2128","authenticated-orcid":false,"given":"Marco","family":"Fontani","sequence":"additional","affiliation":[{"name":"Amped Software, 34149 Trieste, Italy"}]},{"given":"Stefano","family":"Bianchi","sequence":"additional","affiliation":[{"name":"Amped Software, 34149 Trieste, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3047-0519","authenticated-orcid":false,"given":"Alessandro","family":"Piva","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Florence, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4494-3743","authenticated-orcid":false,"given":"Giovanni","family":"Ramponi","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria e Architettura (DIA), Universit\u00e0 Degli Studi di Trieste, 34127 Trieste, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1109\/JSTSP.2020.3002101","article-title":"Media forensics and deepfakes: An overview","volume":"14","author":"Verdoliva","year":"2020","journal-title":"IEEE J. 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