Sparsity-Based Image Inpainting Detection via Canonical Correlation Analysis With Low-Rank Constraints
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Image inpainting, a commonly used image editing technique for filling the mask or missing areas in images, is often adopted to destroy the integrity of images by forgers with ulterior motives. Compared with the other types of inpainting, the sparsity-based inpainting exploits more general prior knowledge and has a broader application scope. Although many methods for detecting exemplar-based and diffusion-based inpainting have been successfully studied in the literature, there is still a lack of effective schemes for detecting the sparsity-based inpainting. In this paper, to fill this gap, we proposed a novel algorithm for sparsity-based image inpainting detection. We revealed the potential connection between sparsity-based inpainting and canonical correlation analysis (CCA). This type of inpainting has a strong effect on the CCA coefficients. Based on this observation, a modified objective function of CCA and a corresponding optimization algorithm are further proposed to enhance the inter-class difference in our feature set. Experimental results on three publicly available data sets demonstrated our method’s superiority over other competitors. Particularly, compared with previous inpainting detection methods, the proposed framework yields better performances in the cases of JPEG compression and Gaussian noise addition. The proposed method also shows promising results when employed to detect other types of inpainting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it