Secure Facial Verification: A hybrid model for detecting Spoof Attacks with ResNet50-DenseNet121
Bibliographic record
Abstract
The present study introduces a novel hybrid deep learning model, leveraging the synergies inherent in the amalgamation of ResNet50 and DenseNet121 architectures. This fusion aims to effectively tackle the formidable task of detecting spoof attacks. Spoof attacks pose a significant threat to digital systems and networks, where adversaries attempt to deceive systems by impersonating legitimate users or sources. The proposed hybrid model aims to enhance detection accuracy and robustness against various spoof attacks by leveraging the complementary features of ResNet50 and DenseNet121. Integrating these architectures creates a unified framework that effectively captures local and global input data features, enabling more comprehensive detection capabilities. The problem of detecting spoofing attacks is stated as a classification task, and we train the hybrid model using large-scale datasets comprising fake and real data samples. The experimental results illustrate the superior performance of the proposed hybrid model in comparison to individual SVM, KNN, CNN, and RNN models, highlighting its efficacy in mitigating the risks associated with spoof attacks in digital systems and networks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".