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Explainable Deep-Fake Detection Using Visual Interpretability Methods

机译：使用可视可解释性方法的可解释深度欺诈检测

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Deep-Fakes have sparked concerns throughout the world because of their potentially explosive consequences. A dystopian future where all forms of digital media are potentially compromised and public trust in Government is scarce doesn't seem far off. If not dealt with the requisite seriousness, the situation could easily spiral out of control. Current methods of Deep-Fake detection aim to accurately solve the issue at hand but may fail to convince a lay-person of its reliability and thus, lack the trust of the general public. Since the fundamental issue revolves around earning the trust of human agents, the construction of interpretable and also easily explainable models is imperative. We propose a framework to detect these Deep-Fake videos using a Deep Learning Approach: we have trained a Convolutional Neural Network architecture on a database of extracted faces from FaceForensics' DeepFakeDetection Dataset. Furthermore, we have tested the model on various Explainable AI techniques such as LRP and LIME to provide crisp visualizations of the salient regions of the image focused on by the model. The prospective and elusive goal is to localize the facial manipulations caused by Faceswaps. We hope to use this approach to build trust between AI and Human agents and to demonstrate the applicability of XAI in various real-life scenarios.
机译：由于其潜在的爆炸性后果，“深造”已引起全世界的关注。在反乌托邦时代，所有形式的数字媒体都可能受到损害，公众对政府的信任不足，这似乎并非遥不可及。如果不处理必要的严肃性，局势很容易失控。当前的Deep-Fake检测方法旨在准确解决当前的问题，但可能无法使外行信服其可靠性，因此缺乏公众的信任。由于基本问题围绕赢得人类代理商的信任，因此必须构建可解释且易于解释的模型。我们提出了一种使用深度学习方法检测这些Deep-Fake视频的框架：我们已经从FaceForensics的DeepFakeDetection数据集提取的面部数据库中训练了卷积神经网络体系结构。此外，我们已经在各种可解释的AI技术（例如LRP和LIME）上测试了该模型，以提供模型关注的图像显着区域的清晰可视化。预期和难以捉摸的目标是定位由Faceswaps引起的面部操作。我们希望使用这种方法在AI和人类代理之间建立信任，并证明XAI在各种现实场景中的适用性。

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