Face Morphing Detection System: A Deep Learning Scheme for Authenticity Identification Based on InceptionV3

This project is the Face-morph-detection-model released by chandu2006-git on GitHub on May 24, 2026. Its core is an end-to-end face morphing detection system built using transfer learning and the InceptionV3 convolutional neural network, which can automatically identify real faces and morphed synthetic faces. It covers image preprocessing, model inference, and an interactive result visualization interface, aiming to solve security issues such as identity theft and passport fraud caused by morphing attacks.

Face morphing attack is a technique that synthesizes a "middle face" from multiple faces to deceive recognition systems. Its harms include identity theft, passport/financial fraud, and border security risks. Detection faces challenges such as visual concealment, diversity of morphing algorithms, image compression masking traces, and real-time requirements.

The system includes three modules: image preprocessing (face detection, alignment, normalization, etc.), model inference (binary classification based on InceptionV3), and a visualization interface. Transfer learning is used to solve the problem of data scarcity, and InceptionV3 is chosen for its strong performance and balanced efficiency. The classification strategy divides images into real/morphed categories and outputs probability scores for threshold adjustment.

The interface design goals are ease of use, intuitiveness, and traceability. It supports image upload (local/drag-and-drop), real-time detection, result visualization (labels, confidence, heatmaps), batch processing, and result export functions.

Development frameworks may include TensorFlow/Keras (model), OpenCV (image processing), Streamlit/Flask (web interface), etc. The training process includes data preparation (division of real + synthetic samples), model construction (replacing the top classifier of pre-trained InceptionV3), optimization (Adam, cross-entropy, early stopping), and evaluation (accuracy, Precision/Recall, F1, ROC-AUC). Deployment considers model size, inference speed, and cross-platform compatibility.

It can be used for enhancing identity verification systems (financial account opening, remote authentication, attendance), ID photo review (automatic preliminary review, manual assistance, batch backtracking), and research and education (reference cases, learning practice).

Current limitations include vulnerability to adversarial attacks, reduced detection effectiveness for unknown morphing algorithms, difficulty with low-quality images, and insufficient robustness to scene diversity. Improvement directions include multi-model integration, attention mechanisms, frequency domain analysis, adversarial training, multi-task learning, and trying the Transformer architecture.

The project demonstrates the application of deep learning in security issues, solves data problems through transfer learning, and provides a full-process example. In the future, it is necessary to deal with more complex face forgery attacks, and technical practitioners should develop defense tools to ensure technology is used for good.