论文标题
图像计数器取证的打印和扫描攻击
Printing and Scanning Attack for Image Counter Forensics
论文作者
论文摘要
随着操纵工具变得更容易访问和先进，检查图像的真实性变得越来越重要。最近的工作表明，尽管基于CNN的图像操纵检测器可以成功识别操作，但它们也容易受到对抗攻击的影响，范围从简单的双重JPEG压缩到基于高级像素的扰动。在本文中，我们探讨了另一种高度合理攻击的方法：打印和扫描。我们证明了两个最先进的模型对这种攻击的脆弱性。我们还提出了一种新的机器学习模型，该模型在对印刷和扫描的图像进行训练和验证时，与这些最先进的模型相当地执行。在这三个模型中，我们提出的模型在对单个打印机的图像进行训练和验证时，优于其他模型。为了促进这种探索，我们创建了一个超过6,000个印刷和扫描的图像块的数据集。进一步的分析表明，从不同打印机产生的图像之间的变化很重要，足够大，以至于来自一台打印机的图像的良好验证精度并不意味着来自不同打印机的相同图像的验证精度相似。
Examining the authenticity of images has become increasingly important as manipulation tools become more accessible and advanced. Recent work has shown that while CNN-based image manipulation detectors can successfully identify manipulations, they are also vulnerable to adversarial attacks, ranging from simple double JPEG compression to advanced pixel-based perturbation. In this paper we explore another method of highly plausible attack: printing and scanning. We demonstrate the vulnerability of two state-of-the-art models to this type of attack. We also propose a new machine learning model that performs comparably to these state-of-the-art models when trained and validated on printed and scanned images. Of the three models, our proposed model outperforms the others when trained and validated on images from a single printer. To facilitate this exploration, we create a dataset of over 6,000 printed and scanned image blocks. Further analysis suggests that variation between images produced from different printers is significant, large enough that good validation accuracy on images from one printer does not imply similar validation accuracy on identical images from a different printer.
