FLIDS: Fuzzy Logic-based Framework for Interpretable Image Manipulation Detection
Bibliographic record
Abstract
This work introduces FLIDS (Fuzzy Logic-based Image Distortion Scoring), an interpretable and efficient system for image tampering detection based on hand-crafted features and fuzzy logic. FLIDS combines JPEG artifact analysis, edge consistency, co-occurrence entropy, and CFA disparities into a fuzzy rule-based system for assigning a tampering confidence score. In contrast to black-box deep learning systems, FLIDS prioritizes transparency and generalizability. Tests on CIFAR-10, MNIST, ImageNet Subset, and Deepfake datasets indicate FLIDS attains competitive accuracy compared to ResNet-18, Autoencoder, and hand-designed JPEG detectors in the majority of instances. FLIDS achieves 93.5% and 91.8% accuracy on CIFAR-10 and ImageNet Subset, respectively, as well as a balanced 90.2% on deepfake datasets. These findings point to FLIDS as a promising, interpretable solution to intricate deep learning systems in image forgery detection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".