This paper proposes an encryption algorithm for crucial areas of a video based on chaos and a neural network, which SVEA (Selective Video Encryption Algorithm). The critical areas of each frame in a video are extracted by deep learning to the encryption system. A one-step encryption algorithm is used to encrypt these critical areas based on chaos, where scrambling and diffusion are simultaneously performed. A new chaotic system 2D extended Schaffer function map (2D-ESFM) is utilized in the encryption system, inspired by the Schaffer function. The system has demonstrated excellent performance through Lyapunov exponents (LEs), permutation entropy (PE), the 0-1 test, and other methods. Additionally, to resist chosen plaintext attacks, the secret key is generated by a neural network, with the critical areas of the video as inputs to the neural network. The chaotic system generates the biases and weights for the neural network. We evaluate SVEA on our dataset (Gymnastics at the Olympic Games) and public datasets. SVEA exhibits strong security characteristics compared to state-of-the-art algorithms and reduces time complexity by approximately 51.3%.

中文翻译：

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使用二维扩展谢弗函数图和神经网络开发关键区域的视频加密算法


本文提出了一种基于混沌和神经网络的视频关键区域加密算法，即SVEA（选择性视频加密算法）。通过深度学习将视频中每一帧的关键区域提取到加密系统中。采用一步加密算法对这些基于混沌的关键区域进行加密，其中加扰和扩散同时进行。受Schaffer函数的启发，加密系统中采用了一种新的混沌系统二维扩展Schaffer函数图（2D-ESFM）。该系统通过Lyapunov指数（LEs）、排列熵（PE）、0-1检验等方法展现了优异的性能。此外，为了抵御选定的明文攻击，密钥由神经网络生成，视频的关键区域作为神经网络的输入。混沌系统为神经网络生成偏差和权重。我们在我们的数据集（奥运会体操）和公共数据集上评估 SVEA。与最先进的算法相比，SVEA 表现出强大的安全特性，并将时间复杂度降低了约 51.3%。