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Modern forensic acquisitions frequently produce multi\u2010terabyte datasets with substantial inter\u2010case redundancy, which significantly affects storage capacity, processing time, and investigation costs. Data deduplication is a potential approach; however, its use in forensic contexts is limited by special operational requirements, such as total data integrity, court\u2010admissible audit trails, and resistance to anti\u2010forensic manipulation. This paper provides a comprehensive survey and evaluation of forensic data deduplication methods. We propose a five\u2010dimensional forensic taxonomy that classifies techniques based on granularity, forensic integrity, temporal placement, verification methodology, and scope &amp; scale. Using multi\u2010criteria decision analysis, we create a Deduplication Reliability Index (DRI) to compare existing systems to forensic requirements statistically. We also systematically identify six types of adversarial threats particular to forensic deduplication, such as hash collision exploitation, deduplication oracle attacks, and reference mapping manipulation. Our analysis shows that file\u2010level and fixed\u2010block deduplication have the highest DRI scores (4.35\u20134.30), indicating that they are optimally suited for forensic adoption, whereas semantic, probabilistic, and lossy techniques score less than 3.0 due to poor verification and integrity assurances. The report concludes by proposing crucial research goals, such as standardized validation frameworks, adversarial\u2010resilient deduplication structures, and efficient zero\u2010knowledge verification algorithms for forensics.<\/jats:p>","DOI":"10.1111\/1556-4029.70480","type":"journal-article","created":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T06:43:12Z","timestamp":1790404992000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Forensic data deduplication: A comprehensive survey of methods and challenges for digital investigation"],"prefix":"10.1111","author":[{"given":"Gunikhan","family":"Sonowal","sequence":"first","affiliation":[{"name":"ISEA National Forensic Sciences University (NFSU)  Gandhinagar India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Parag","family":"Rughani","sequence":"additional","affiliation":[{"name":"ISEA National Forensic Sciences University (NFSU)  Gandhinagar India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2121-7500","authenticated-orcid":false,"given":"Gaurav","family":"Gogia","sequence":"additional","affiliation":[{"name":"MONAKA Fujitsu Research of India Private Limited  Bengaluru India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.diin.2014.09.002"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2019.2962586"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3154059"},{"key":"e_1_2_12_5_1","unstructured":"Page McBeth.Digital forensics labs are drowning: 4 ways investigators can step in.2025. 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