Byte-level generative predictions for forensics multimedia carving
Digital forensic investigations often face significant challenges when recovering fragmented multimedia files that lack file system metadata. While traditional file carving relies on signatures and discriminative deep learning models for fragment classification, these methods cannot reconstruct or predict missing data. We propose a generative approach to multimedia carving using bGPT, a byte-level transformer designed for next-byte prediction. By feeding partial BMP image data into the model, we simulate the generation of likely fragment continuations. We evaluate the fidelity of these predictions using different metrics, namely, cosine similarity, structural similarity index (SSIM), chi-...
| Authors | Jaewon Lee, Md Eimran Hossain Eimon, Avinash Srinivasan et al. |
|---|---|
| Primary category | cs.CV |
| Published | 13 April 2026 |
| Last updated at source | 13 April 2026 |
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