EfficientNet-Based Multi-Class Detection of Real, Deepfake, and Plastic Surgery Faces

2025-09-12

AI Summary

Deepfakes and cosmetic surgery–altered faces pose emerging threats to identity authentication, privacy security, and public trust. Existing detection methods predominantly adopt binary classification (real vs. fake), neglecting surgically modified faces as a distinct category. Method: We propose a fine-grained three-class detection framework—distinguishing real, deepfake, and cosmetically altered faces—built upon EfficientNet with transfer learning and multi-strategy data augmentation. Contribution/Results: First, we formally define cosmetically altered faces as an independent detection class, addressing a critical gap in prior deepfake detection research. Second, we introduce a feature disentanglement and discriminative enhancement mechanism tailored for three-way classification, improving cross-domain generalization. Extensive experiments on heterogeneous, multi-source datasets demonstrate an average accuracy of 98.2%, substantially outperforming binary baselines and exhibiting strong robustness and practical utility in real-world scenarios.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Currently, deep learning has been utilised to tackle several difficulties in our everyday lives. It not only exhibits progress in computer vision but also constitutes the foundation for several revolutionary technologies. Nonetheless, similar to all phenomena, the use of deep learning in diverse domains has produced a multifaceted interaction of advantages and disadvantages for human society. Deepfake technology has advanced, significantly impacting social life. However, developments in this technology can affect privacy, the reputations of prominent personalities, and national security via software development. It can produce indistinguishable counterfeit photographs and films, potentially impairing the functionality of facial recognition systems, so presenting a significant risk. The improper application of deepfake technology produces several detrimental effects on society. Face-swapping programs mislead users by altering persons' appearances or expressions to fulfil particular aims or to appropriate personal information. Deepfake technology permeates daily life through such techniques. Certain individuals endeavour to sabotage election campaigns or subvert prominent political figures by creating deceptive pictures to influence public perception, causing significant harm to a nation's political and economic structure.

Problem

• Detecting real, deepfake, and plastic surgery faces
• Addressing deepfake's privacy and security threats
• Mitigating risks to facial recognition systems

Innovation

• EfficientNet-based multi-class face detection
• Deepfake and plastic surgery identification
• Computer vision deep learning solution