用多层CNN识别伪造人脸图像，准确率达76%。
Detecting Facial Image Manipulations with Multi-Layer CNN Models
- 设计三种复杂度递增的CNN模型检测人脸篡改
- 在多种修改下实现最高76%的识别准确率
- 适合需要轻量级验证工具的研究者和安全团队
数字图像伪造技术的快速发展给内容真实性验证带来严峻挑战，如Stable Diffusion和Mid-Journey生成的图像高度逼真且为合成图像，容易欺骗人类感知。本研究开发并评估了专用于检测此类篡改图像的卷积神经网络（CNN）模型。通过对比三种逐步复杂的CNN架构，评估其在各类人脸图像修改下的分类与定位能力。系统性地引入正则化与优化技术以提升特征提取与性能表现。结果显示，所提模型在区分伪造图像与真实图像方面准确率最高达76%，优于传统方法。该研究不仅展示了CNN在增强数字媒体验证工具鲁棒性方面的潜力，还提供了面向低算力环境的有效架构调整与训练策略。未来工作将基于此扩展模型以应对更多样化的篡改手段，并融合多模态数据以提升检测能力。
原文摘要 · Abstract (English)
The rapid evolution of digital image manipulation techniques poses significant challenges for content verification, with models such as stable diffusion and mid-journey producing highly realistic, yet synthetic, images that can deceive human perception. This research develops and evaluates convolutional neural networks (CNNs) specifically tailored for the detection of these manipulated images. The study implements a comparative analysis of three progressively complex CNN architectures, assessing their ability to classify and localize manipulations across various facial image modifications. Regularization and optimization techniques were systematically incorporated to improve feature extraction and performance. The results indicate that the proposed models achieve an accuracy of up to 76\% in distinguishing manipulated images from genuine ones, surpassing traditional approaches. This research not only highlights the potential of CNNs in enhancing the robustness of digital media verification tools, but also provides insights into effective architectural adaptations and training strategies for low-computation environments. Future work will build on these findings by extending the architectures to handle more diverse manipulation techniques and integrating multi-modal data for improved detection capabilities.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。