融合空间与频域特征，用双分支网络提升图像伪造检测准确率
Dual-Branch Convolutional Framework for Spatial and Frequency-Based Image Forgery Detection
- 设计双分支卷积网络，分别处理空间和频域特征
- 在CASIA 2.0数据集上达到77.9%准确率，优于传统方法
- 轻量结构适合实际部署，适用于媒体真实性验证
随着深度伪造和数字图像篡改的迅速增加，确保图像真实性变得愈发困难。本文提出一种结合空间与频域特征的伪造检测框架，采用双分支卷积神经网络分别处理来自空间域和频率域的特征。两分支特征在孪生网络中融合并比较，生成64维嵌入用于分类。在CASIA 2.0数据集上的实验表明，该方法准确率达77.9%，优于传统统计方法。尽管相比更大更复杂的检测系统性能稍弱，但本方法在计算复杂度与检测可靠性之间取得良好平衡，具备实际部署潜力。该研究为数字图像司法鉴定提供了有效方法，推动了视觉取证领域发展，满足媒体验证、执法及数字内容可信性等迫切需求。
原文摘要 · Abstract (English)
With a very rapid increase in deepfakes and digital image forgeries, ensuring the authenticity of images is becoming increasingly challenging. This report introduces a forgery detection framework that combines spatial and frequency-based features for detecting forgeries. We propose a dual branch convolution neural network that operates on features extracted from spatial and frequency domains. Features from both branches are fused and compared within a Siamese network, yielding 64 dimensional embeddings for classification. When benchmarked on CASIA 2.0 dataset, our method achieves an accuracy of 77.9%, outperforming traditional statistical methods. Despite its relatively weaker performance compared to larger, more complex forgery detection pipelines, our approach balances computational complexity and detection reliability, making it ready for practical deployment. It provides a strong methodology for forensic scrutiny of digital images. In a broader sense, it advances the state of the art in visual forensics, addressing an urgent requirement in media verification, law enforcement and digital content reliability.
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