The orientation in which a source image is captured can affect the resulting security in downstream applications. One reason for this is that many state-of-the-art methods in media security assume that image statistics are similar in the horizontal and vertical directions, allowing them to reduce the number of features (or trainable weights) by merging coefficients. We show that this artificial symmetrization tends to suppress important properties of natural images and common processing operations, causing a loss of performance. We also observe the opposite problem, where unaddressed directionality causes learning-based methods to overfit to a single orientation. These are vulnerable to manipulation if an adversary chooses inputs with the less common orientation. This paper takes a comprehensive approach, identifies and systematizes causes of directionality at several stages of a typical acquisition pipeline, measures their effect, and demonstrates for three selected security applications (steganalysis, forensic source identification, and the detection of synthetic images) how the performance of state-of-the-art methods can be improved by properly accounting for directionality.

翻译：源图像的拍摄方向会影响其在后续应用中的安全性。原因之一是当前许多媒体安全领域的前沿方法假设图像在水平与垂直方向上的统计特性相似，从而通过合并系数来减少特征（或可训练权重）数量。我们证明，这种人为对称化往往会抑制自然图像及常见处理操作的重要特性，导致性能下降。同时我们也观察到相反的问题：未被处理的方向性会导致基于学习的方法过度拟合单一方向。若攻击者选择使用较少见方向的输入，此类方法将易受操控。本文采用系统性研究方法，识别并归类了典型采集流程中多个阶段产生方向性的原因，测量了其影响，并以三个具体安全应用（隐写分析、取证来源识别与合成图像检测）为例，展示了通过恰当处理方向性如何提升前沿方法的性能。