Image Inpainting Detection Based on a Modified Formulation of Canonical Correlation Analysis
Bibliographic record
Abstract
Image inpainting is a common image editing technique for filling the missing areas in images. It can be adopted to destroy the integrity of images by forgers with ulterior motives. Compared with other types of inpainting, sparsity-based inpainting assumes more general prior knowledge and is more widely used in practical applications. Although several methods for detecting exemplar-based and diffusion-based inpainting have been proposed, there is a shortage of effective scheme for detecting sparsity-based inpainting. In this paper, we proposed a novel algorithm for sparsity-based image inpainting detection. This type of inpainting has a strong effect on the coefficients of Canonical Correlation Analysis (CCA). Based on this observation, a modified objective function of CCA and a corresponding optimization algorithm are further developed to enhance the difference of inter-class in our feature set. The experiments implemented on two publicly available datasets demonstrated our method's superiority over other competitors. Particularly, unlike previous inpainting detection methods, the proposed framework has better performance in the case of JPEG compression.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".