Backdoor attacks allow an attacker to embed a specific vulnerability in a
machine learning algorithm, activated when an attacker-chosen pattern is
presented, causing a specific misprediction. The need to identify backdoors in
biometric scenarios has led us to propose a novel technique with different
trade-offs. In this paper we propose to use model pairs on open-set
classification tasks for detecting backdoors. Using a simple linear operation
to project embeddings from a probe model's embedding space to a reference
model's embedding space, we can compare both embeddings and compute a
similarity score. We show that this score, can be an indicator for the presence
of a backdoor despite models being of different architectures, having been
trained independently and on different datasets. Additionally, we show that
backdoors can be detected even when both models are backdoored. The source code
is made available for reproducibility purposes.

我们提出了一种使用模型对开放集分类任务的技术来检测后门攻击，该技术可以在不同体系结构、独立训练且使用不同数据集的模型中检测到后门攻击，并通过线性操作比较嵌入向量并计算相似度得分。

使用嵌入翻译进行模型配对，检测开放集分类任务中的后门攻击

Model Pairing Using Embedding Translation for Backdoor Attack Detection  on Open-Set Classification Tasks

Generative Adversarial Networks (GANs) are shown to be successful at
generating new and realistic samples including 3D object models. Conditional
GAN, a variant of GANs, allows generating samples in given conditions. However,
objects generated for each condition are different and it does not allow
generation of the same object in different conditions. In this paper, we first
adapt conditional GAN, which is originally designed for 2D image generation, to
the problem of generating 3D models in different rotations. We then propose a
new approach to guide the network to generate the same 3D sample in different
and controllable rotation angles (sample pairs). Unlike previous studies, the
proposed method does not require modification of the standard conditional GAN
architecture and it can be integrated into the training step of any conditional
GAN. Experimental results and visual comparison of 3D models show that the
proposed method is successful at generating model pairs in different
conditions.

本文研究了在生成条件之下如何进行三维模型的生成，并提出了一种新的方法来使得在不同旋转角度下生成的同一模型相同。实验结果表明该方法可以成功地生成不同条件下的模型对。