Among steganalysis techniques, detection against motion vector (MV)
domain-based video steganography in High Efficiency Video Coding (HEVC)
standard remains a hot and challenging issue. For the purpose of improving the
detection performance, this paper proposes a steganalysis feature based on the
optimality of predicted MVs with a dimension of one. Firstly, we point out that
the motion vector prediction (MVP) of the prediction unit (PU) encoded using
the Advanced Motion Vector Prediction (AMVP) technique satisfies the local
optimality in the cover video. Secondly, we analyze that in HEVC video, message
embedding either using MVP index or motion vector differences (MVD) may destroy
the above optimality of MVP. And then, we define the optimal rate of MVP in
HEVC video as a steganalysis feature. Finally, we conduct steganalysis
detection experiments on two general datasets for three popular steganography
methods and compare the performance with four state-of-the-art steganalysis
methods. The experimental results show that the proposed optimal rate of MVP
for all cover videos is 100\%, while the optimal rate of MVP for all stego
videos is less than 100\%. Therefore, the proposed steganography scheme can
accurately distinguish between cover videos and stego videos, and it is
efficiently applied to practical scenarios with no model training and low
computational complexity.

基于高效视频编码中运动矢量优化的隐写分析特征，本文提出一种能够准确识别覆盖视频和隐写视频的隐写方案，并且在无需模型训练和低计算复杂度的实际场景中表现高效。

使用预测运动矢量的优化性质的一维 HEVC 视频隐写分析方法

A One-dimensional HEVC video steganalysis method using the Optimality of  Predicted Motion Vectors

In recent years, NLP practitioners have converged on the following practice:
(i) import an off-the-shelf pretrained (masked) language model; (ii) append a
multilayer perceptron atop the CLS token's hidden representation (with randomly
initialized weights); and (iii) fine-tune the entire model on a downstream task
(MLP). This procedure has produced massive gains on standard NLP benchmarks,
but these models remain brittle, even to mild adversarial perturbations, such
as word-level synonym substitutions. In this work, we demonstrate surprising
gains in adversarial robustness enjoyed by Model-tuning Via Prompts (MVP), an
alternative method of adapting to downstream tasks. Rather than modifying the
model (by appending an MLP head), MVP instead modifies the input (by appending
a prompt template). Across three classification datasets, MVP improves
performance against adversarial word-level synonym substitutions by an average
of 8% over standard methods and even outperforms adversarial training-based
state-of-art defenses by 3.5%. By combining MVP with adversarial training, we
achieve further improvements in robust accuracy while maintaining clean
accuracy. Finally, we conduct ablations to investigate the mechanism underlying
these gains. Notably, we find that the main causes of vulnerability of MLP can
be attributed to the misalignment between pre-training and fine-tuning tasks,
and the randomly initialized MLP parameters. Code is available at
this https URL

该研究通过 Model-tuning Via Prompts (MVP) 修改输入而非模型以适应下游任务，在三个分类数据集上提高对抗性的稳健性，超过标准方法平均 8％和对抗性训练的最新防御技术 3.5％。研究结果发现多层感知器 (MLP) 易受对抗性扰动的影响归因于预训练和微调任务之间的不匹配和 MLP 参数的随机初始化。