File Fragment Type Classification Using Light-Weight Convolutional Neural Networks
Bibliographic record
Abstract
In digital forensics, file carving is used to extract files without relying on the underlying file system metadata. This process can be challenging if the file is fragmented. Therefore, it is important first to identify the type of file fragment. There exist several techniques to identify the type of file fragments without relying on metadata, for example, using headers and footers to identify the fragment type. Recently, convolutional neural network (CNN) models have been used to build classification models to achieve this task. Existing models for file fragment type classification often require significant computational resources due to their large number of parameters, leading to slower inference times and higher memory consumption. To address these challenges, we propose light-weight file fragment type classification models based on separable CNNs that maintain comparable accuracy while reducing computational demands. Our proposed light-weight file fragment type classification model leverages depthwise separable convolutions to improve the efficiency of feature extraction while reducing computational overhead. This approach leads to improved classification performance by focusing on the most relevant features within file fragments, achieving comparable accuracy to state-of-the-art models with significantly fewer parameters. The evaluation results demonstrate the model’s effectiveness, with a 79% accuracy on the FFT-75 dataset using nearly 100K parameters and 164M FLOPs —representing a 4x reduction in model size and a 6x improvement in speed over the best-performing existing classifier. Our results demonstrate that these light-weight models are effective for real-time digital forensic applications where computational efficiency is critical.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".