Synthetic multimedia content created through AI technologies, such as Generative Adversarial Networks (GAN), applied to human faces can bring serious social and political consequences in the private life of every person. State-of-the-art algorithms use deep neural networks to detect a fake content but unfortunately almost all approaches appear to be neither generalizable nor explainable. A new fast detection method able to discriminate Deepfake images with blazing speed and high precision is exposed. By employing Discrete Cosine Transform (DCT), anomalous frequencies in real and Deepfake datasets were analyzed. The \beta statistics inferred by the AC coefficients distribution have been the key to recognize GAN-engine generated data. The technique has been validated on pristine high quality faces synthesized by different GANs architectures. Experiments carried out show that the method is innovative, exceeds the state-of-the-art and also gives many insights in terms of explainability.
翻译:通过人工智能技术创造的合成多媒体内容,例如适用于人的基因反转网络(GAN),可能会给每个人的私生活带来严重的社会和政治后果。最先进的算法利用深神经网络检测假内容,但不幸的是,几乎所有方法似乎都不普遍,也不易解释。一种新的快速检测方法能够以闪亮的速度和高精度对深造图像进行区分。通过使用分辨孔变换(DCT),对真实和深法克数据集中的异常频率进行了分析。由AC系数分布推算的 \beta 统计数据一直是识别GAN-engine生成数据的关键。该技术在由不同GANs结构合成的纯质高面孔上得到了验证。所进行的实验表明,该方法具有创新性,超越了最新技术,也从可解释性的角度提供了许多洞察力。