Video forgery detection is one of the challenges in this digital era, where the focus is on discovering authenticity. Though there are so many methods available to detect forgeries in the video, there is no method that utilizes illumination-based forgery detection. Hence, this research focuses on establishing the 3D model of the video frame to generate light coefficients in order to detect the forgeries in the video. On the other hand, this paper proposes dual adaptive-Taylor-rider optimization algorithm-based deep convolutional neural network (DA-Taylor-ROA-based DCNN) for video forgery detection, where DCNN is trained using the dual adaptive-Taylor-rider optimization algorithm (DA-TROA) that inherits the adaptive concept and Taylor series within the standard rider optimization algorithm (ROA). For the detection process, the distance-based features from the light coefficients and face objects detected using the Viola–Jones algorithm from the video frames are used. The significance of the method is analyzed using the real images for varying noise conditions based on the performance metrics, such as accuracy, true positive rate, and true negative rate. The percentage improvement of accuracy for proposed DA-Taylor-ROA-based DCNN with respect to Taylor-ROA-Based deep CNN is 4.3626% in the absence of noise, and 1.5985% of accuracy improvement in the presence of speckle noise, respectively.

中文翻译：

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用于 3D 照明环境中视频伪造检测的双自适应深度卷积神经网络

视频伪造检测是这个数字时代的挑战之一，重点是发现真实性。虽然有很多方法可用于检测视频中的伪造，但没有一种方法可以利用基于光照的伪造检测。因此，本研究的重点是建立视频帧的 3D 模型以生成光系数，以检测视频中的伪造。另一方面，本文提出了基于双重自适应泰勒骑士优化算法的深度卷积神经网络（DA-Taylor-ROA-based DCNN）用于视频伪造检测，其中使用双重自适应泰勒骑士优化训练DCNN算法（DA-TROA）继承了标准骑手优化算法（ROA）中的自适应概念和泰勒级数。对于检测过程，使用 Viola-Jones 算法从视频帧中检测到的光系数和面部对象的基于距离的特征。基于性能指标，如准确率、真阳性率和真阴性率，使用真实图像对变化的噪声条件分析该方法的重要性。在没有噪声的情况下，提出的基于 DA-Taylor-ROA 的 DCNN 相对于基于 Taylor-ROA 的深度 CNN 的精度提高百分比分别为 4.3626%，在存在斑点噪声的情况下精度提高了 1.5985%。和真负率。在没有噪声的情况下，提出的基于 DA-Taylor-ROA 的 DCNN 相对于基于 Taylor-ROA 的深度 CNN 的精度提高百分比分别为 4.3626%，在存在斑点噪声的情况下精度提高了 1.5985%。和真负率。在没有噪声的情况下，提出的基于 DA-Taylor-ROA 的 DCNN 相对于基于 Taylor-ROA 的深度 CNN 的精度提高百分比分别为 4.3626%，在存在斑点噪声的情况下精度提高了 1.5985%。