In the context of temporal image forensics, it is not evident that a neural network, trained on images from different time-slots (classes), exploits solely image age related features. Usually, images taken in close temporal proximity (e.g., belonging to the same age class) share some common content properties. Such content bias can be exploited by a neural network. In this work, a novel approach is proposed that evaluates the influence of image content. This approach is verified using synthetic images (where content bias can be ruled out) with an age signal embedded. Based on the proposed approach, it is shown that a deep learning approach proposed in the context of age classification is most likely highly dependent on the image content. As a possible countermeasure, two different models from the field of image steganalysis, along with three different preprocessing techniques to increase the signal-to-noise ratio (age signal to image content), are evaluated using the proposed method.

翻译：在时序图像取证背景下，神经网络利用不同时间段（类别）图像进行训练时，其是否仅依赖于与图像年龄相关的特征并非显而易见。通常，在时间上接近（例如属于同一年龄类别）的图像会共享某些共同的内容属性。这种内容偏差可能被神经网络利用。本文提出了一种新颖的方法来评估图像内容的影响，并通过嵌入年龄信号的合成图像（可排除内容偏差）验证了该方法的有效性。基于所提出的方法，研究表明，在年龄分类背景下采用的深度学习方法极可能高度依赖图像内容。作为潜在的应对措施，本文利用所提方法评估了图像隐写分析领域的两种不同模型，以及三种用于提高信噪比（年龄信号与图像内容之比）的预处理技术。