Current deep learning-based steganalyzers often depend on specific image dimensions, leading to inevitable adjustments in network structure when dealing with varied image sizes. This impedes their effectiveness in managing the wide range of image sizes commonly found on social media. To address this issue, our paper presents a novel steganalytic network that is optimized for fixed-size (notably, $256\times 256$ ) color images, but is capable of efficiently detecting stego images of arbitrary size without needing retraining or modifications to the network. Our proposed network is comprised of four modules. In the initial stem module, we calculate truncated residuals for each color channel of the input image. Diverging from existing steganalytic networks that rely on vanilla convolution, we have developed a pixel difference convolution module designed to better capture the artifacts introduced by steganography. Following this, we introduce an enhanced Transformer module with selective pooling, aimed at more effectively extracting global steganalytic features. To guarantee our network’s adaptability to different image sizes, we have developed a selective pooling strategy. This involves using global covariance pooling for fixed-size color images and spatial pyramid pooling for color images of various other sizes. This approach effectively standardizes the feature maps into uniform feature vectors. The final module is focused on classification. Extensive testing results on the ALASKA II color image dataset have demonstrated that our approach significantly improves detection performance for both fixed-size and arbitrary-size images, achieving state-of-the-art results. Additionally, we provide numerous ablation studies to confirm the effectiveness and soundness of our proposed network architecture.

中文翻译：

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基于像素差分卷积和具有选择性池化的增强 Transformer 的彩色图像隐写分析


当前基于深度学习的 steganalyzer 通常依赖于特定的图像尺寸，导致在处理不同的图像尺寸时不可避免地需要调整网络结构。这阻碍了他们管理社交媒体上常见的各种图像尺寸的有效性。为了解决这个问题，我们提出了一种新的隐写分析网络，它针对固定大小（特别是$256\times 256$ ）的彩色图像进行了优化，但能够有效地检测任意大小的隐写图像，而无需重新训练或修改网络。我们提议的网络由四个模块组成。在初始 stem 模块中，我们计算输入图像的每个颜色通道的截断残差。与依赖普通卷积的现有隐写分析网络不同，我们开发了一个像素差分卷积模块，旨在更好地捕获隐写术引入的伪影。在此之后，我们引入了一个具有选择性池化功能的增强 Transformer 模块，旨在更有效地提取全局隐写分析特征。为了保证我们的网络对不同图像大小的适应性，我们开发了一种选择性池化策略。这涉及对固定大小的彩色图像使用全局协方差池化，对各种其他大小的彩色图像使用空间金字塔池化。这种方法有效地将特征图标准化为统一的特征向量。最后一个模块侧重于分类。对 ALASKA II 彩色图像数据集的广泛测试结果表明，我们的方法显著提高了固定大小和任意大小图像的检测性能，获得了最先进的结果。 此外，我们还提供了大量消融研究，以确认我们提出的网络架构的有效性和健全性。