It is not only sufficient to construct computational models that can accurately classify or detect fake images from real images taken from a camera, but it is also important to ensure whether these computational models are fair enough or produce biased outcomes that can eventually harm certain social groups or cause serious security threats. Exploring fairness in forensic algorithms is an initial step towards correcting these biases. Since visual transformers are recently being widely used in most image classification based tasks due to their capability to produce high accuracies, this study tries to explore bias in the transformer based image forensic algorithms that classify natural and GAN generated images. By procuring a bias evaluation corpora, this study analyzes bias in gender, racial, affective, and intersectional domains using a wide set of individual and pairwise bias evaluation measures. As the generalizability of the algorithms against image compression is an important factor to be considered in forensic tasks, this study also analyzes the role of image compression on model bias. Hence to study the impact of image compression on model bias, a two phase evaluation setting is followed, where a set of experiments is carried out in the uncompressed evaluation setting and the other in the compressed evaluation setting.

翻译：构建能够准确分类或检测相机拍摄真实图像与伪造图像的计算模型不仅必要，还需确保这些模型具备充分公平性，避免产生可能损害特定社会群体或引发严重安全威胁的偏见结果。探究取证算法中的公平性是纠正这些偏见的初始步骤。鉴于视觉Transformer因其高精度性能近期被广泛用于多数基于图像分类的任务，本研究尝试探索基于Transformer的图像取证算法中分类自然图像与生成对抗网络（GAN）生成图像时存在的偏见。通过构建偏见评估语料库，本研究采用一系列个体与成对偏见评估指标，在性别、种族、情感及交叉领域维度分析偏见。由于算法对图像压缩的泛化能力是取证任务中的重要考量因素，本研究同时分析了图像压缩对模型偏见的影响。为研究图像压缩对模型偏见的影响，实验采用两阶段评估设置：一组在无压缩环境下进行，另一组在压缩环境下进行。