Deepfake technology is widely used, which has led to serious worries about the authenticity of digital media, making the need for trustworthy deepfake face recognition techniques more urgent than ever. This study employs a resource-effective and transparent cost-sensitive deep learning method to effectively detect deepfake faces in videos. To create a reliable deepfake detection system, four pre-trained Convolutional Neural Network (CNN) models: XceptionNet, InceptionResNetV2, EfficientNetV2S, and EfficientNetV2M were used. FaceForensics++ and CelebDf-V2 as benchmark datasets were used to assess the performance of our method. To efficiently process video data, key frame extraction was used as a feature extraction technique. Our main contribution is to show the models adaptability and effectiveness in correctly identifying deepfake faces in videos. Furthermore, a cost-sensitive neural network method was applied to solve the dataset imbalance issue that arises frequently in deepfake detection. The XceptionNet model on the CelebDf-V2 dataset gave the proposed methodology a 98% accuracy, which was the highest possible whereas, the InceptionResNetV2 model, achieves an accuracy of 94% on the FaceForensics++ dataset. Source Code: https://github.com/Faysal-MD/Unmasking-Deepfake-Faces-from-Videos-An-Explainable-Cost-Sensitive-Deep-Learning-Approach-IEEE2023

翻译：深度伪造技术被广泛使用，引发了人们对数字媒体真实性的严重担忧，这使得对可信赖的深度伪造人脸识别技术的需求比以往任何时候都更加紧迫。本研究采用一种资源高效且透明的成本敏感深度学习方法，有效检测视频中的深度伪造人脸。为构建可靠的深度伪造检测系统，使用了四种预训练的卷积神经网络模型：XceptionNet、InceptionResNetV2、EfficientNetV2S和EfficientNetV2M。使用FaceForensics++和CelebDf-V2作为基准数据集来评估我们方法的性能。为高效处理视频数据，采用关键帧提取作为特征提取技术。我们的主要贡献在于展示了模型正确识别视频中深度伪造人脸的适应性和有效性。此外，应用成本敏感神经网络方法来解决深度伪造检测中频繁出现的数据集不平衡问题。在CelebDf-V2数据集上，XceptionNet模型为所提方法提供了98%的准确率，这是可能达到的最高值；而在FaceForensics++数据集上，InceptionResNetV2模型达到了94%的准确率。源代码：https://github.com/Faysal-MD/Unmasking-Deepfake-Faces-from-Videos-An-Explainable-Cost-Sensitive-Deep-Learning-Approach-IEEE2023