Deep Fake Image Classification Using VGG-19 Model
Why this work is in the frame
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Bibliographic record
Abstract
Fake images, also known as "deepfakes," are a growing concern in today's digital age.These images are often created with the intent of benefiting one party and can be difficult to distinguish from real images.They are often disseminated through digital media and newspapers, and can spread misinformation or propaganda, which can have serious consequences if not detected and addressed.To effectively detect image falsification in many image data, an architectural model that can process several pixels in the image is required, as well as a method that is effective and adjustable with training data for practical use in daily life.In this paper to detecting fake images usingVGG19 is a convolutional neural network (CNN) architecture that has been successful in a variety of image classification tasks.The proposed VGG19 is better model compared existing models it provides 96% accuracy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.022 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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