In this paper, we present a novel approach for generating naturalistic
adversarial patches without using GANs. Our proposed approach generates a
Dynamic Adversarial Patch (DAP) that looks naturalistic while maintaining high
attack efficiency and robustness in real-world scenarios. To achieve this, we
redefine the optimization problem by introducing a new objective function,
where a similarity metric is used to construct a similarity loss. This guides
the patch to follow predefined patterns while maximizing the victim model's
loss function. Our technique is based on directly modifying the pixel values in
the patch which gives higher flexibility and larger space to incorporate
multiple transformations compared to the GAN-based techniques. Furthermore,
most clothing-based physical attacks assume static objects and ignore the
possible transformations caused by non-rigid deformation due to changes in a
person's pose. To address this limitation, we incorporate a ``Creases
Transformation'' (CT) block, i.e., a preprocessing block following an
Expectation Over Transformation (EOT) block used to generate a large variation
of transformed patches incorporated in the training process to increase its
robustness to different possible real-world distortions (e.g., creases in the
clothing, rotation, re-scaling, random noise, brightness and contrast
variations, etc.). We demonstrate that the presence of different real-world
variations in clothing and object poses (i.e., above-mentioned distortions)
lead to a drop in the performance of state-of-the-art attacks. For instance,
these techniques can merely achieve 20\% in the physical world and 30.8\% in
the digital world while our attack provides superior success rate of up to 65\%
and 84.56\%, respectively when attacking the YOLOv3tiny detector deployed in
smart cameras at the edge.

本文提出了一种新颖的方法来生成自然的对抗性补丁，该方法不使用 GANs 并通过引入相似度指标构建相似度损失来重新定义优化问题，从而提高实际应用中的攻击效率和鲁棒性。该技术基于直接修改补丁中的像素值，可灵活地同时纳入多种变换。加入 “Creases Transformation”（CT）模块来增加对服装和物体姿态中的各种真实世界扭曲的鲁棒性，并在现场部署的智能相机的 YOLOv3tiny 检测器中展现出了优越的攻击成功率。

DAP: 一种用于规避人物检测器的动态对抗贴纸

DAP: A Dynamic Adversarial Patch for Evading Person Detectors

When building state-of-the-art speech translation models, the need for large
computational resources is a significant obstacle due to the large training
data size and complex models. The availability of pre-trained models is a
promising opportunity to build strong speech translation systems efficiently.
In a first step, we investigate efficient strategies to build cascaded and
end-to-end speech translation systems based on pre-trained models. Using this
strategy, we can train and apply the models on a single GPU. While the
end-to-end models show superior translation performance to cascaded ones, the
application of this technology has a limitation on the need for additional
end-to-end training data. In a second step, we proposed an additional
similarity loss to encourage the model to generate similar hidden
representations for speech and transcript. Using this technique, we can
increase the data efficiency and improve the translation quality by 6 BLEU
points in scenarios with limited end-to-end training data.

论文探究使用预训练模型构建高效的语音翻译模型，提出基于相似损失的方法以增强数据效率及提高翻译质量。