基于深度卷积神经网络的浮雕赝品识别
Deep Convolutional Neural Network for Identifying Seam-Carving Forgery
摘要：
接缝雕刻是一种具有代表性的内容感知图像重定向方法，用于调整图像的大小，同时保留其视觉上突出的内容。为了保持视觉上重要的内容，接缝雕刻算法首先根据定义的成本函数计算像素的连接路径（称为接缝），然后通过删除和复制重复计算的接缝来调整图像的大小。接缝雕刻被积极地利用来克服应用程序和设备之间的图像分辨率的差异;因此，检测由接缝雕刻引起的失真在图像取证中变得重要。在本文中，我们提出了一个卷积神经网络（CNN）为基础的方法来分类缝雕刻为基础的图像重定向减少和扩大。为了获得学习低级特征的能力，我们设计了一个CNN架构，该架构包括五种类型的网络块，专门用于捕获细微信号。进一步采用集成模块来提高性能并全面分析给定图像的局部区域中的特征。为了验证我们的工作的有效性，基于各种基于CNN的基线进行了广泛的实验。与基线相比，我们的工作在三级分类（原始，接缝插入和接缝移除）方面表现出最先进的性能。此外，我们的模型与合奏模块是强大的各种看不见的情况下。实验结果还表明，我们的方法可以应用于定位接缝删除和接缝插入区域。
英文摘要：
Seam carving is a representative content-aware image retargeting approach to adjust the size of an image while preserving its visually prominent content. To maintain visually important content, seam-carving algorithms first calculate the connected path of pixels, referred to as the seam, according to a defined cost function and then adjust the size of an image by removing and duplicating repeatedly calculated seams. Seam carving is actively exploited to overcome diversity in the resolution of images between applications and devices; hence, detecting the distortion caused by seam carving has become important in image forensics. In this paper, we propose a convolutional neural network (CNN)-based approach to classifying seam-carving-based image retargeting for reduction and expansion. To attain the ability to learn low-level features, we designed a CNN architecture comprising five types of network blocks specialized for capturing subtle signals. An ensemble module is further adopted to both enhance performance and comprehensively analyze the features in the local areas of the given image. To validate the effectiveness of our work, extensive experiments based on various CNN-based baselines were conducted. Compared to the baselines, our work exhibits state-of-the-art performance in terms of three-class classification (original, seam inserted, and seam removed). In addition, our model with the ensemble module is robust for various unseen cases. The experimental results also demonstrate that our method can be applied to localize both seam-removed and seam-inserted areas.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
