Recently, on the internet, the level of image and video forgery has augmented due to the augmentation in the malware, which has facilitated user (anyone) to upload, download, or share objects online comprising audio, images, or video. Recently, Convolution Neural Network (CNN) has turn into a de-facto technique for classification of multi-dimensional data and it renders standard and also highly effectual network layer arrangements. But these architectures are limited by the speed due to massive number of calculations needed for training in addition to testing the network and also, it might render less accuracy. To trounce these issues, this paper proposed to ameliorate the image and video forgery detection’s efficiency utilizing hybrid CNN. Initially, the intensive along with incremental learning phase is carried out. After that, the hybrid CNN is implemented to detect the image together with video forgery. The developed system was tested on images together with videos for different kinds of forgeries, and it was observed that the proposed work obtains more than 98% accuracy for both testing as well as validation sets.

中文翻译：

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使用混合卷积神经网络提高图像和视频伪造检测的效率

最近，在互联网上，由于恶意软件的增强，图像和视频的伪造水平有所提高，这有助于用户（任何人）在线上传、下载或共享包含音频、图像或视频的对象。最近，卷积神经网络 (CNN) 已成为一种事实上的多维数据分类技术，它呈现标准且高效的网络层排列。但是这些架构受到速度的限制，因为除了测试网络之外，训练还需要大量的计算，而且它可能会降低准确性。为了解决这些问题，本文提出利用混合 CNN 提高图像和视频伪造检测的效率。最初，进行强化学习和增量学习阶段。在那之后，混合 CNN 用于检测图像和视频伪造。所开发的系统在图像和不同类型的伪造视频上进行了测试，观察到所提出的工作在测试集和验证集上都获得了超过 98% 的准确率。