ID Card Spoofing Detection Using Frequency Features and CNNs
Bibliographic record
Abstract
This paper proposes a lightweight and robust approach for detecting spoofed ID card images by integrating convolutional neural networks (CNNs) with frequency-domain analysis.The model adopts a dual-branch design: one branch processes the original RGB image, while the other takes a frequency-enhanced version produced using a high-pass Fast Fourier Transform (FFT) filter.Both branches use the same architecture: the first seven convolutional layers of the VGG16 backbone but each branch has its own parameters.The two streams are merged by a multi-head cross-attention fusion module, which aligns and integrates the complementary cues from both branches more effectively, followed by a classification module for "genuine" vs. "spoof".The method is evaluated on the "or" and "re" subsets of the Document Liveness Challenge 2021 dataset (DLC-2021).On these subsets, the model attains precision of 93.64%, recall of 88.90% and accuracy of 91.68%, significantly outperforming baseline models.The implementation remains computationally efficient, requiring about 0.120 s per image on an Intel Xeon 2.20 GHz (x86-64) CPU.The approach achieves a favorable trade-off by combining high detection accuracy with a compact model size.These results highlight the benefit of exploiting both spatial and frequency features to enhance the reliability of electronic identity verification systems.
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.
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 | 0.002 | 0.001 |
| 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) | 0.001 | 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".