Content Bias in Deep Learning Image Age Approximation: A new Approach Towards better Explainability

TL;DR

This paper investigates how deep learning models for image age estimation may rely on image content bias rather than age features, proposing a new method to evaluate content influence and testing countermeasures.

Contribution

It introduces a novel approach to assess content bias in deep learning age estimation models and evaluates techniques to mitigate this bias.

Findings

Deep learning models are highly influenced by image content in age classification.

Synthetic images confirm content bias can be isolated from age signals.

Preprocessing techniques can improve the age signal-to-noise ratio.

Abstract

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.