Traditional video steganography methods are based on modifying the covert
space for embedding, whereas we propose an innovative approach that embeds
secret message within semantic feature for steganography during the video
editing process. Although existing traditional video steganography methods
display a certain level of security and embedding capacity, they lack adequate
robustness against common distortions in online social networks (OSNs). In this
paper, we introduce an end-to-end robust generative video steganography network
(RoGVS), which achieves visual editing by modifying semantic feature of videos
to embed secret message. We employ face-swapping scenario to showcase the
visual editing effects. We first design a secret message embedding module to
adaptively hide secret message into the semantic feature of videos. Extensive
experiments display that the proposed RoGVS method applied to facial video
datasets demonstrate its superiority over existing video and image
steganography techniques in terms of both robustness and capacity.

通过在视频编辑过程中将秘密信息嵌入语义特征，提出了一种创新的视频隐写术方法。引入了端到端的鲁棒性生成式视频隐写术网络（RoGVS），通过修改视频的语义特征来实现视觉编辑和嵌入秘密信息，通过人脸替换场景展示了其视觉编辑效果。实验证明，RoGVS 方法在面部视频数据集上的应用在鲁棒性和容量两方面优于现有的视频和图像隐写术技术。

从隐蔽隐藏到视觉编辑：强大的生成式视频隐写术

From Covert Hiding to Visual Editing: Robust Generative Video  Steganography

Textual entailment recognition is one of the basic natural language
understanding(NLU) tasks. Understanding the meaning of sentences is a
prerequisite before applying any natural language processing(NLP) techniques to
automatically recognize the textual entailment. A text entails a hypothesis if
and only if the true value of the hypothesis follows the text. Classical
approaches generally utilize the feature value of each word from word embedding
to represent the sentences. In this paper, we propose a novel approach to
identifying the textual entailment relationship between text and hypothesis,
thereby introducing a new semantic feature focusing on empirical
threshold-based semantic text representation. We employ an element-wise
Manhattan distance vector-based feature that can identify the semantic
entailment relationship between the text-hypothesis pair. We carried out
several experiments on a benchmark entailment classification(SICK-RTE) dataset.
We train several machine learning(ML) algorithms applying both semantic and
lexical features to classify the text-hypothesis pair as entailment, neutral,
or contradiction. Our empirical sentence representation technique enriches the
semantic information of the texts and hypotheses found to be more efficient
than the classical ones. In the end, our approach significantly outperforms
known methods in understanding the meaning of the sentences for the textual
entailment classification task.

本文提出了一种新的基于阈值的语义文本表示方法，并结合机器学习算法应用语义和词汇特征对文本 - 假设对进行分类，该技术有效地丰富了文本的语义信息，在文字蕴涵分类任务中在意义理解方面的表现明显优于已知方法

通过经验文本表示的语义特征实现文本蕴涵识别

Textual Entailment Recognition with Semantic Features from Empirical Text Representation

Grounded Situation Recognition (GSR) is the task that not only classifies a
salient action (verb), but also predicts entities (nouns) associated with
semantic roles and their locations in the given image. Inspired by the
remarkable success of Transformers in vision tasks, we propose a GSR model
based on a Transformer encoder-decoder architecture. The attention mechanism of
our model enables accurate verb classification by capturing high-level semantic
feature of an image effectively, and allows the model to flexibly deal with the
complicated and image-dependent relations between entities for improved noun
classification and localization. Our model is the first Transformer
architecture for GSR, and achieves the state of the art in every evaluation
metric on the SWiG benchmark. Our code is available at
this https URL .

本文将基于 Transformer 编码解码框架提出一种 Grounded Situation Recognition 模型，通过有效地捕获图像的高层语义特征实现动词的精确定位，进而在处理实体间复杂的、依赖于图像的关系时进行名词的分类和定位，实现了该领域的最新进展，并在 SWiG 基准测试中实现了优异的表现。