Synthetic image attribution addresses the problem of tracing back the origin
of images produced by generative models. Extensive efforts have been made to
explore unique representations of generative models and use them to attribute a
synthetic image to the model that produced it. Most of the methods classify the
models or the architectures among those in a closed set without considering the
possibility that the system is fed with samples produced by unknown
architectures. With the continuous progress of AI technology, new generative
architectures continuously appear, thus driving the attention of researchers
towards the development of tools capable of working in open-set scenarios. In
this paper, we propose a framework for open set attribution of synthetic
images, named BOSC (Backdoor-based Open Set Classification), that relies on the
concept of backdoor attacks to design a classifier with rejection option. BOSC
works by purposely injecting class-specific triggers inside a portion of the
images in the training set to induce the network to establish a matching
between class features and trigger features. The behavior of the trained model
with respect to triggered samples is then exploited at test time to perform
sample rejection using an ad-hoc score. Experiments show that the proposed
method has good performance, always surpassing the state-of-the-art. Robustness
against image processing is also very good. Although we designed our method for
the task of synthetic image attribution, the proposed framework is a general
one and can be used for other image forensic applications.

基于背门攻击的开放集分类器的合成图像溯源方法 (BOSC)，通过注入类特定触发器来诱导网络建立类特征与触发特征的匹配，以此设计分类器并进行样本拒绝的方法。在实验证明该方法在合成图像溯源任务中表现良好，超过了最先进技术，并且对图像处理具有很好的鲁棒性，同时该框架还可应用于其他图像取证应用领域。

BOSC: 一个基于后门的开放集合合成图像归属框架

BOSC: A Backdoor-based Framework for Open Set Synthetic Image  Attribution

In the realm of automated robotic surgery and computer-assisted
interventions, understanding robotic surgical activities stands paramount.
Existing algorithms dedicated to surgical activity recognition predominantly
cater to pre-defined closed-set paradigms, ignoring the challenges of
real-world open-set scenarios. Such algorithms often falter in the presence of
test samples originating from classes unseen during training phases. To tackle
this problem, we introduce an innovative Open-Set Surgical Activity Recognition
(OSSAR) framework. Our solution leverages the hyperspherical reciprocal point
strategy to enhance the distinction between known and unknown classes in the
feature space. Additionally, we address the issue of over-confidence in the
closed set by refining model calibration, avoiding misclassification of unknown
classes as known ones. To support our assertions, we establish an open-set
surgical activity benchmark utilizing the public JIGSAWS dataset. Besides, we
also collect a novel dataset on endoscopic submucosal dissection for surgical
activity tasks. Extensive comparisons and ablation experiments on these
datasets demonstrate the significant outperformance of our method over existing
state-of-the-art approaches. Our proposed solution can effectively address the
challenges of real-world surgical scenarios. Our code is publicly accessible at
this https URL

在自动机器人手术和计算机辅助干预领域中，理解机器人手术活动至关重要。为了解决真实世界中的开放场景挑战，我们引入了一种创新的开集手术活动识别（OSSAR）框架，通过使用超球面互补点策略增强特征空间中已知和未知类之间的差异，并通过改进模型校准来避免未知类被误分类为已知类。通过公开的 JIGSAWS 数据集以及我们自己收集的内窥镜黏膜下剥离的手术活动数据集，我们对我们的方法进行了广泛的对比和割离实验，结果表明我们的方法在现有最先进方法上取得了显著的提升。我们的方案可以有效处理真实世界手术场景的挑战。我们的代码可以在此 https URL 上公开访问。

面向机器人辅助手术的开放式外科活动识别

OSSAR: Towards Open-Set Surgical Activity Recognition in Robot-assisted  Surgery

Semi-supervised learning (SSL) methods effectively leverage unlabeled data to
improve model generalization. However, SSL models often underperform in
open-set scenarios, where unlabeled data contain outliers from novel categories
that do not appear in the labeled set. In this paper, we study the challenging
and realistic open-set SSL setting, where the goal is to both correctly
classify inliers and to detect outliers. Intuitively, the inlier classifier
should be trained on inlier data only. However, we find that inlier
classification performance can be largely improved by incorporating
high-confidence pseudo-labeled data, regardless of whether they are inliers or
outliers. Also, we propose to utilize non-linear transformations to separate
the features used for inlier classification and outlier detection in the
multi-task learning framework, preventing adverse effects between them.
Additionally, we introduce pseudo-negative mining, which further boosts outlier
detection performance. The three ingredients lead to what we call Simple but
Strong Baseline (SSB) for open-set SSL. In experiments, SSB greatly improves
both inlier classification and outlier detection performance, outperforming
existing methods by a large margin. Our code will be released at
this https URL

研究了半监督学习在开放集场景下的表现，提出了一种名为 Simple but Strong Baseline (SSB) 的方法，通过使用高置信度伪标签数据和非线性转换来改善内部分类和异常检测性能，进一步引入了伪负样本挖掘以提升对异常数据的检测。