A Robust Algorithm for Digital Image Copyright Protection and Tampering Detection: Employing DWT, DCT, and Blowfish Techniques
Bibliographic record
Abstract
With the rapid proliferation of digital images on the internet, the task of preserving image ownership and ensuring the detection of unauthorized alterations has become increasingly challenging. This study introduces a robust algorithm, leveraging Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Blowfish encryption techniques, designed to maintain copyright integrity and detect image tampering. The proposed algorithm operates on a given RGB host image, first isolating it into its constituent red, green, and blue components. For the purpose of copyright protection, the algorithm applies DWT and DCT to the green component, embedding a watermark logo within it. The blue component is subjected to Blowfish encryption, generating a ciphered blue component that aids in tampering detection. Subsequently, the least significant bits of this ciphered blue component are interchanged with those of the host image's red component, producing a novel red component. This process results in the creation of a watermarked green component, an original blue component, and a newly formed red component. These are then amalgamated to produce the final watermarked image. The proposed method is evaluated using five standard images, with simulation results demonstrating its resilience to various attacks. Importantly, the algorithm exhibits a capacity to detect any unauthorized modifications up to a granularity of 22 pixels.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".