Online video platforms receive hundreds of hours of uploads every minute,
making manual content moderation impossible. Unfortunately, the most vulnerable
consumers of malicious video content are children from ages 1-5 whose attention
is easily captured by bursts of color and sound. Scammers attempting to
monetize their content may craft malicious children's videos that are
superficially similar to educational videos, but include scary and disgusting
characters, violent motions, loud music, and disturbing noises. Prominent video
hosting platforms like YouTube have taken measures to mitigate malicious
content on their platform, but these videos often go undetected by current
content moderation tools that are focused on removing pornographic or
copyrighted content. This paper introduces our toolkit Malicious or Benign for
promoting research on automated content moderation of children's videos. We
present 1) a customizable annotation tool for videos, 2) a new dataset with
difficult to detect test cases of malicious content and 3) a benchmark suite of
state-of-the-art video classification models.

本研究介绍了一款工具，可以自动检测儿童视频是否存在恶意内容，包括可定制注释工具、数据集和基准测试套件。

恶意还是良性？针对儿童视频的有效内容管理

Malicious or Benign? Towards Effective Content Moderation for Children's  Videos

Learning an egocentric action recognition model from video data is
challenging due to distractors (e.g., irrelevant objects) in the background.
Further integrating object information into an action model is hence
beneficial. Existing methods often leverage a generic object detector to
identify and represent the objects in the scene. However, several important
issues remain. Object class annotations of good quality for the target domain
(dataset) are still required for learning good object representation. Besides,
previous methods deeply couple the existing action models and need to retrain
them jointly with object representation, leading to costly and inflexible
integration. To overcome both limitations, we introduce Self-Supervised
Learning Over Sets (SOS), an approach to pre-train a generic Objects In Contact
(OIC) representation model from video object regions detected by an
off-the-shelf hand-object contact detector. Instead of augmenting object
regions individually as in conventional self-supervised learning, we view the
action process as a means of natural data transformations with unique
spatio-temporal continuity and exploit the inherent relationships among
per-video object sets. Extensive experiments on two datasets, EPIC-KITCHENS-100
and EGTEA, show that our OIC significantly boosts the performance of multiple
state-of-the-art video classification models.

采用自助学习技术的 “自我监督学习 over sets”(SOS) 方法，从视频数据的对象区域中提取表示对象的图像特征，结合自然数据转换过程中的时空连续性和对象集内在关系，实现了人物自身操作的行为识别，显著提升了多个最先进的视频分类模型的性能。

SOS！基于自我监督学习的被处理物体集合在视角动作识别中的应用

SOS! Self-supervised Learning Over Sets Of Handled Objects In Egocentric Action Recognition

When compared to the image classification models, black-box adversarial
attacks against video classification models have been largely understudied.
This could be possible because, with video, the temporal dimension poses
significant additional challenges in gradient estimation. Query-efficient
black-box attacks rely on effectively estimated gradients towards maximizing
the probability of misclassifying the target video. In this work, we
demonstrate that such effective gradients can be searched for by parameterizing
the temporal structure of the search space with geometric transformations.
Specifically, we design a novel iterative algorithm Geometric TRAnsformed
Perturbations (GEO-TRAP), for attacking video classification models. GEO-TRAP
employs standard geometric transformation operations to reduce the search space
for effective gradients into searching for a small group of parameters that
define these operations. This group of parameters describes the geometric
progression of gradients, resulting in a reduced and structured search space.
Our algorithm inherently leads to successful perturbations with surprisingly
few queries. For example, adversarial examples generated from GEO-TRAP have
better attack success rates with ~73.55% fewer queries compared to the
state-of-the-art method for video adversarial attacks on the widely used Jester
dataset. Overall, our algorithm exposes vulnerabilities of diverse video
classification models and achieves new state-of-the-art results under black-box
settings on two large datasets. Code is available here:
this https URL

本文介绍了一种新的迭代算法 Geometric TRAnsformed Perturbations (GEO-TRAP)，使用几何变换来降低有效梯度的搜索空间，从而实现黑盒攻击视频分类模型。该算法在广泛使用的 Jester 数据集上的攻击成功率比最先进的视频对抗攻击方法少了约 73.55% 的查询次数，同时在两个大型数据集上战胜了现有的黑盒攻击设置下的最新结果。