AI 中文总结
研究针对图像伪造检测，提出用CPU计算和梯度提升树的轻量级特征工程管道。通过多尺度ELA等特征构建405维向量，经格式控制评估，在仅JPEG子集上有高AUC和F1值，消融研究明确各特征作用，实现检测压缩历史不一致且具多种优势。
AI 中文摘要
图像伪造检测是数字取证中的关键任务，许多深度学习定位方法通常由GPU加速，计算量比手工筛选方法大。我们提出了一种轻量级、可解释的特征工程管道，仅使用CPU计算和梯度提升树进行图像级伪造筛查。我们的方法引入了在七个JPEG质量级别计算的多尺度误差级别分析（ELA），并结合了新颖的交叉质量ELA比率特征，该特征捕获拼接区域的双重压缩伪像，并通过空间熵、FFT能带、边缘密度、SRM残差和DCT块度进行增强，产生一个405维特征向量。CASIA v2.0存在格式混淆问题，通过严格的格式控制评估，在仅JPEG子集上，我们的方法在5折分层交叉验证中实现了AUC = 0.990 [95% CI: 0.988--0.991]和F1 = 0.905。消融研究表明，多尺度ELA提供了主要增益，交叉质量比率提供了互补的双重压缩检测。这些结果支持该方法检测压缩历史不一致性，同时提供特征级可解释性、仅CPU部署和亚秒级推理。
英文摘要
Image forgery detection is a critical task in digital forensics, yet many deep-learning localization approaches are typically GPU-accelerated and computationally heavier than handcrafted screening methods. We propose a lightweight, interpretable feature engineering pipeline for image-level forgery screening using only CPU computation and gradient boosted trees. Our method introduces \emph{multi-scale Error Level Analysis} (ELA) computed at seven JPEG quality levels, combined with novel \emph{cross-quality ELA ratio} features that capture double-compression artifacts characteristic of spliced regions, augmented by spatial entropy, FFT energy bands, edge density, SRM residuals, and DCT blockiness, yielding a 405-dimensional feature vector. CASIA v2.0 contains a format confound (60\% of tampered images are TIFF while authentic images are JPEG/BMP and contain no TIFF samples), enabling a trivial \texttt{is\_tiff} classifier to reach 0.80 AUC. We address this through rigorous format-controlled evaluation: on the JPEG-only subset (9,501 images, eliminating the TIFF/JPEG container confound), our method achieves AUC~=~0.990 [95\% CI: 0.988--0.991] and F1~=~0.905 using 5-fold stratified cross-validation. Under a conservative source-aware group split (preventing related images from appearing in both train and test), AUC remains 0.976. An ablation study reveals that multi-scale ELA provides the dominant gain (+0.180 AUC over single-quality on the format-controlled subset), while cross-quality ratios provide complementary double-compression detection. These results support that the method detects compression-history inconsistencies rather than file-format shortcuts -- while offering feature-level interpretability, CPU-only deployment, and sub-second inference.
CommentsThis work has been submitted to the IEEE for possible publication
