Abstract
Deepfake is a widely used technology employed in recent years to create pernicious content such as fake news, movies, and rumors by altering and substituting facial information from various sources. Given the ongoing evolution of deepfakes investigation of continuous identification and prevention is crucial. Due to recent technological advancements in AI (Artificial Intelligence) distinguishing deepfakes and artificially altered images has become challenging. This approach introduces the robust detection of subtle ear movements and shape changes to generate ear descriptors. Further, we also propose a novel optimized hybrid deepfake detection model that considers the ear biometric descriptors via enhanced RCNN (Region-Based Convolutional Neural Network). Initially, the input video is converted into frames and preprocessed through resizing, normalization, grayscale conversion, and filtering processes followed by face detection using the Viola-Jones technique. Next, a hybrid model comprising DBN (Deep Belief Network) and Bi-GRU (Bidirectional Gated Recurrent Unit) is utilized for deepfake detection based on ear descriptors. The output from the detection phase is determined through improved score-level fusion. To enhance the performance, the weights of both detection models are optimally tuned using the SU-JFO (Self-Upgraded Jellyfish Optimization method). Experimentation is conducted based on four scenarios: compression, noise, rotation, pose, and illumination on three different datasets. The performance results affirm that our proposed method outperforms traditional models such as CNN (Convolution Neural Network), SqueezeNet, LeNet, LinkNet, LSTM (Long Short-Term Memory), DFP (Deepfake Predictor) [1], and ResNext+CNN+LSTM [2] in terms of various performance metrics viz. accuracy, specificity, and precision.
Abstract (translated)
近年来，Deepfake技术被广泛用于生成虚假新闻、电影和谣言等内容，这种技术通过改变和替换来自各种来源的面部信息来制造有害内容。鉴于深伪视频调查的持续识别与预防变得愈发重要，在人工智能（AI）领域的最新技术进步使得区分深度伪造视频和人工修改过的图像变得更加困难。此方法提出了一种用于生成耳部描述符的稳健检测手段，以捕捉细微的耳部动作变化及形态改变。 此外，我们还提出了一种新颖优化的混合型Deepfake检测模型，该模型通过增强版RCNN（基于区域的卷积神经网络）考虑了生物特征中的耳部信息。首先，输入视频被转换为帧并通过调整大小、标准化、灰度化和滤波等预处理过程进行处理，然后使用Viola-Jones技术进行面部检测。 接下来，采用包含DBN（深度信念网络）与Bi-GRU（双向门控循环单元）的混合模型基于耳部描述符来进行深伪视频识别。检测阶段的结果通过改进后的评分级融合确定。为提升性能，我们利用SU-JFO（自我升级水母优化方法）对两种检测模型的权重进行最优调整。 实验基于三个不同的数据集，在压缩、噪声、旋转、姿势以及光照五个场景下进行了测试。结果表明，我们的方法在各种性能指标（如准确性、特异性和精确度）方面优于传统模型，例如CNN（卷积神经网络）、SqueezeNet、LeNet、LinkNet、LSTM（长短期记忆）、DFP（Deepfake预测器）[1]以及ResNext+CNN+LSTM [2]。
URL
https://arxiv.org/abs/2503.12381
