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It involves analyzing evidence from electronic devices, social media, and system logs. However, investigators face major challenges due to the large size of digital data. Information from networks, smartphones, and online platforms is often too complex to analyze within a limited time. The large size of digital evidence and the pressure of a strict timeline for case investigation force the investigator to do data reduction and selective analysis. It is also difficult for the investigator to correlate the various digital evidence to create the complete picture of the crime story. To address these challenges, we propose a Multimodal Artificial Intelligence Framework designed to integrate heterogeneous digital evidence analysis capabilities by leveraging artificial intelligence. The proof\u2010of\u2010concept architecture proposed in the paper incorporates explainable AI, pattern recognition, and a confidence\u2010aware inference mechanism for semantic evidence analysis. The experimental analysis of the proposed model shows improved investigative efficiency. The analysis of the research results endorses the effectiveness of AI\u2010assisted workflow in digital forensics investigation.<\/jats:p>","DOI":"10.1111\/1556-4029.70497","type":"journal-article","created":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T08:14:21Z","timestamp":1791360861000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A multimodal artificial intelligence framework for digital forensics"],"prefix":"10.1111","author":[{"given":"Manish","family":"Kumar","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering RV University  Bangalore India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,7]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1145\/3267357.3267365","volume-title":"Proceedings of the 2nd international workshop on multimedia privacy and security (MPS '18)","author":"Li S","year":"2018"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.1561\/0600000096"},{"key":"e_1_2_13_4_1","first-page":"1","volume-title":"Proceedings of the 13th international conference on availability, reliability and security (ARES)","author":"Bouchaud F","year":"2018"},{"key":"e_1_2_13_5_1","first-page":"1","volume-title":"2021 international conference on digital futures and transformative technologies (ICoDT2)","author":"Qadir S","year":"2021"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/wfs2.1418"},{"key":"e_1_2_13_7_1","first-page":"1","volume-title":"2020 8th international symposium on digital forensics and security (ISDFS)","author":"Qadir AM","year":"2020"},{"key":"e_1_2_13_8_1","first-page":"1","volume-title":"Proceedings of the ACM Symposium on Document Engineering","author":"Collomosse J","year":"2018"},{"key":"e_1_2_13_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3029280"},{"key":"e_1_2_13_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12027\u2010022\u201000702\u2010z"},{"key":"e_1_2_13_11_1","first-page":"19","volume-title":"Proceedings of the 2018 international conference on machine learning technologies (ICMLT '18)","author":"Wang Y","year":"2018"},{"key":"e_1_2_13_12_1","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/4942657"},{"key":"e_1_2_13_13_1","doi-asserted-by":"publisher","DOI":"10.30564\/jcsr.v2i3.2144"},{"key":"e_1_2_13_14_1","unstructured":"HuoY ZhuX.High dynamic range image forensics using CNN. arXiv [Preprint];2019. 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