In this paper, we study the performance invariance of convolutional neural networks when confronted with variable image sizes in the context of a more "wild steganalysis". First, we propose two algorithms and definitions for a fine experimental protocol with datasets owning "similar difficulty" and "similar security". The "smart crop 2" algorithm allows the introduction of the Nearly Nested Image Datasets (NNID) that ensure "a similar difficulty" between various datasets, and a dichotomous research algorithm allows a "similar security". Second, we show that invariance does not exist in state-of-the-art architectures. We also exhibit a difference in behavior depending on whether we test on images larger or smaller than the training images. Finally, based on the experiments, we propose to use the dilated convolution which leads to an improvement of a state-of-the-art architecture.

翻译：本文研究了卷积神经网络在“野隐写分析”背景下，面对可变图像尺寸时的性能不变性。首先，我们提出了两种算法及相应定义，用于构建具有“相似难度”和“相似安全性”数据集的精细实验方案。"智能裁剪2"算法能够生成近似嵌套图像数据集（NNID），确保不同数据集间具有“相似难度”；而二分搜索算法则可实现“相似安全性”。其次，我们证明现有最优架构中并不存在这种不变性，同时揭示了测试图像尺寸大于或小于训练图像时的行为差异。最后，基于实验分析，我们提出采用扩张卷积以改进现有最优架构的性能。