This research evaluates a convolutional neural network (CNN) based approach
to forensic video steganalysis. A video steganography dataset is created to
train a CNN to conduct forensic steganalysis in the spatial domain. We use a
noise residual convolutional neural network to detect embedded secrets since a
steganographic embedding process will always result in the modification of
pixel values in video frames. Experimental results show that the CNN-based
approach can be an effective method for forensic video steganalysis and can
reach a detection rate of 99.96%. Keywords: Forensic, Steganalysis, Deep
Steganography, MSU StegoVideo, Convolutional Neural Networks

该研究评估了一种卷积神经网络（CNN）的方法来进行法医视频隐写分析，使用一个噪声残差的卷积神经网络来检测嵌入式的秘密，实验结果表明，CNN 方法可以是一种有效的法医视频隐写分析方法，可以达到 99.96% 的检测率。

利用噪声残差卷积神经网络进行空间域取证视频隐写分析

Forensic Video Steganalysis in Spatial Domain by Noise Residual  Convolutional Neural Network

The booming interest in adversarial attacks stems from a misalignment between
human vision and a deep neural network (DNN), i.e. a human imperceptible
perturbation fools the DNN. Moreover, a single perturbation, often called
universal adversarial perturbation (UAP), can be generated to fool the DNN for
most images. A similar misalignment phenomenon has recently also been observed
in the deep steganography task, where a decoder network can retrieve a secret
image back from a slightly perturbed cover image. We attempt explaining the
success of both in a unified manner from the Fourier perspective. We perform
task-specific and joint analysis and reveal that (a) frequency is a key factor
that influences their performance based on the proposed entropy metric for
quantifying the frequency distribution; (b) their success can be attributed to
a DNN being highly sensitive to high-frequency content. We also perform feature
layer analysis for providing deep insight on model generalization and
robustness. Additionally, we propose two new variants of universal
perturbations: (1) Universal Secret Adversarial Perturbation (USAP) that
simultaneously achieves attack and hiding; (2) high-pass UAP (HP-UAP) that is
less visible to the human eye.

利用傅里叶分析的方法，我们从任务特定和联合分析的角度，揭示频率分布和高频内容对深度学习模型的鲁棒性和发现模型广泛化有深层次的影响。我们还提出了两种新的万能扰动：同时达到攻击和隐藏的 “通用秘密对抗扰动”（USAP）和对人眼不可见的 “高通万能扰动”（HP-UAP）