ChaSAM: An Architecture Based on Perceptual Hashing for Image Detection in Computer Forensics
Bibliographic record
Abstract
The growing prevalence of digital crimes, especially those involving Child Sexual Abuse Material (CSAM) and revenge pornography, highlights the need for advanced forensic techniques to identify and analyze illicit content. While cryptographic hashing is commonly used in computer forensics, its effectiveness is often challenged because criminals can modify original information to create a new cryptographic hash. Perceptual hashes address this problem by focusing on the visual identity of the file rather than its bit-by-bit representation. This study introduces ChaSAM Forensics, a methodology that efficiently identifies illicit material using perceptual hashing techniques to track and identify illicit content, with a focus on child abuse material. Two new perceptual hashing algorithms, chHash and domiHash, were designed for integration into ChaSAM. The results showed that, under the tested conditions, the proposed chHash algorithm was more accurate than the established pHash algorithm when applied in a single iteration. Combinations of algorithms in two iterations were also assessed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".