对抗性训练已被证明是捍卫对抗性例子的最有效的补救措施之一，但通常会遭受在看不见的测试对手身上巨大的稳定性概括差距，被认为是\ emph {对抗性强大的概括性问题}。尽管最初是针对对抗性强大的概括的初步理解，但从建筑的角度来看，知之甚少。因此，本文试图通过系统地检查最具代表性的体系结构（例如，视觉变压器和CNN）来弥合差距。特别是，我们首先对Imagenette和CIFAR-10数据集进行了对抗训练的架构\ Emph {20}对几个对手（多个$ \ ell_p $ -norm -norm对照攻击）的架构，并发现视觉变形金刚（例如，PVT，Coatnet）经常产生更好的对抗性稳定性。为了进一步了解哪种建筑成分有利于对抗性的强大概括，我们深入研究了几个关键的构建块，并通过Rademacher复杂性的镜头揭示了这一事实，即更高的重量稀疏性对更好的对手的视觉变形金刚的强大良好概括有很大贡献，这通常可以实现这一目标，这是可以实现的。通过注意层。我们的广泛研究发现了建筑设计与对抗性稳定的概括之间的密切关系，并实例化了一些重要的见解。我们希望我们的发现可以帮助更好地理解设计强大的深度学习体系结构的机制。

知识蒸馏（KD）显示了其对象检测的有效性，在AI知识（教师检测器）和人类知识（人类专家）的监督下，它在该物体检测中训练紧凑的对象检测器。但是，现有研究一致地对待AI知识和人类知识，并在学习过程中采用统一的数据增强策略，这将导致对多尺度对象的学习有偏见，并且对教师探测器的学习不足，从而导致不满意的蒸馏性能。为了解决这些问题，我们提出了特定于样本的数据增强和对抗性功能增强。首先，为了减轻多尺度对象产生的影响，我们根据傅立叶角度的观察结果提出了自适应数据增强。其次，我们提出了一种基于对抗性示例的功能增强方法，以更好地模仿AI知识以弥补教师探测器的信息不足。此外，我们提出的方法是统一的，并且很容易扩展到其他KD方法。广泛的实验证明了我们的框架的有效性，并在一阶段和两阶段探测器中提高了最先进方法的性能，最多可以带来0.5 MAP的增长。

最近的研究表明，即使在攻击者无法访问模型信息的黑匣子场景中，基于深模型的检测器也容易受到对抗示例的影响。大多数现有的攻击方法旨在最大程度地减少真正的积极速率，这通常显示出较差的攻击性能，因为在受攻击的边界框中可以检测到另一个最佳的边界框成为新的真实积极的框架。为了解决这一挑战，我们建议最大程度地降低真实的正速率并最大化误报率，这可以鼓励更多的假阳性对象阻止新的真实正面边界框的产生。它被建模为多目标优化（MOP）问题，通用算法可以搜索帕累托最佳选择。但是，我们的任务具有超过200万个决策变量，导致搜索效率较低。因此，我们将标准的遗传算法扩展到了随机子集选择和称为GARSDC的分裂和矛盾，从而显着提高了效率。此外，为了减轻通用算法中人口质量的敏感性，我们利用具有相似骨架的不同检测器之间的可转移性产生了梯度优先人口。与最先进的攻击方法相比，GARSDC在地图中平均减少12.0，在广泛的实验中查询约1000倍。我们的代码可以在https://github.com/liangsiyuan21/ garsdc找到。

对抗训练（AT）方法有效地防止对抗性攻击，但它们在不同阶级之间引入了严重的准确性和鲁棒性差异，称为强大的公平性问题。以前建议的公平健壮的学习（FRL）适应重新重量不同的类别以提高公平性。但是，表现良好的班级的表现降低了，导致表现强劲。在本文中，我们在对抗训练中观察到了两种不公平现象：在产生每个类别的对抗性示例（源级公平）和产生对抗性示例时（目标级公平）时产生对抗性示例的不​​同困难。从观察结果中，我们提出平衡对抗训练（BAT）来解决强大的公平问题。关于源阶级的公平性，我们调整了每个班级的攻击强度和困难，以在决策边界附近生成样本，以便更容易，更公平的模型学习；考虑到目标级公平，通过引入统一的分布约束，我们鼓励每个班级的对抗性示例生成过程都有公平的趋势。在多个数据集（CIFAR-10，CIFAR-100和IMAGENETTE）上进行的广泛实验表明，我们的方法可以显着超过其他基线，以减轻健壮的公平性问题（最坏的类精度为+5-10 \％）

大量证据表明，深神经网络（DNN）容易受到后门攻击的影响，这激发了后门检测方法的发展。现有的后门检测方法通常是针对具有单个特定类型（例如基于补丁或基于扰动）的后门攻击而定制的。但是，在实践中，对手可能会产生多种类型的后门攻击，这挑战了当前的检测策略。基于以下事实：对抗性扰动与触发模式高度相关，本文提出了自适应扰动生成（APG）框架，以通过自适应注射对抗性扰动来检测多种类型的后门攻击。由于不同的触发模式在相同的对抗扰动下显示出高度多样的行为，因此我们首先设计了全球到本地策略，以通过调整攻击的区域和预算来适应多种类型的后门触发器。为了进一步提高扰动注入的效率，我们引入了梯度引导的掩模生成策略，以寻找最佳区域以进行对抗攻击。在多个数据集（CIFAR-10，GTSRB，Tiny-Imagenet）上进行的广泛实验表明，我们的方法以大幅度优于最先进的基线（+12％）。

有条件的生成模型旨在学习数据和标签的基础联合分布，以实现有条件的数据生成。其中，辅助分类器生成的对抗网络（AC-GAN）已被广泛使用，但遭受了生成样品的阶层内多样性的问题。本文指出的基本原因是，AC-GAN的分类器是生成器 - 静脉器，因此不能为发电机提供接近联合分布的信息指导，从而最小化条件熵，从而减少了阶级内的阶级。多样性。在这种理解的推动下，我们提出了一个具有辅助判别分类器（ADC-GAN）的新型条件gan，以解决上述问题。具体而言，提出的辅助判别分类器通过识别真实数据的类标签和生成的数据而成为生成器感知。我们的理论分析表明，即使没有原始歧视者，发电机也可以忠实地学习联合分布，从而使拟议的ADC-GAN可靠，可适应该系数超参数的价值和GAN损失的选择，并在训练过程中稳定。关于合成和现实世界数据集的广泛实验结果表明，与基于最新的分类器和基于基于投影的条件gan相比，有条件生成建模中ADC-GAN的优势。

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.

Decompilation aims to transform a low-level program language (LPL) (eg., binary file) into its functionally-equivalent high-level program language (HPL) (e.g., C/C++). It is a core technology in software security, especially in vulnerability discovery and malware analysis. In recent years, with the successful application of neural machine translation (NMT) models in natural language processing (NLP), researchers have tried to build neural decompilers by borrowing the idea of NMT. They formulate the decompilation process as a translation problem between LPL and HPL, aiming to reduce the human cost required to develop decompilation tools and improve their generalizability. However, state-of-the-art learning-based decompilers do not cope well with compiler-optimized binaries. Since real-world binaries are mostly compiler-optimized, decompilers that do not consider optimized binaries have limited practical significance. In this paper, we propose a novel learning-based approach named NeurDP, that targets compiler-optimized binaries. NeurDP uses a graph neural network (GNN) model to convert LPL to an intermediate representation (IR), which bridges the gap between source code and optimized binary. We also design an Optimized Translation Unit (OTU) to split functions into smaller code fragments for better translation performance. Evaluation results on datasets containing various types of statements show that NeurDP can decompile optimized binaries with 45.21% higher accuracy than state-of-the-art neural decompilation frameworks.

Image Virtual try-on aims at replacing the cloth on a personal image with a garment image (in-shop clothes), which has attracted increasing attention from the multimedia and computer vision communities. Prior methods successfully preserve the character of clothing images, however, occlusion remains a pernicious effect for realistic virtual try-on. In this work, we first present a comprehensive analysis of the occlusions and categorize them into two aspects: i) Inherent-Occlusion: the ghost of the former cloth still exists in the try-on image; ii) Acquired-Occlusion: the target cloth warps to the unreasonable body part. Based on the in-depth analysis, we find that the occlusions can be simulated by a novel semantically-guided mixup module, which can generate semantic-specific occluded images that work together with the try-on images to facilitate training a de-occlusion try-on (DOC-VTON) framework. Specifically, DOC-VTON first conducts a sharpened semantic parsing on the try-on person. Aided by semantics guidance and pose prior, various complexities of texture are selectively blending with human parts in a copy-and-paste manner. Then, the Generative Module (GM) is utilized to take charge of synthesizing the final try-on image and learning to de-occlusion jointly. In comparison to the state-of-the-art methods, DOC-VTON achieves better perceptual quality by reducing occlusion effects.

Due to their ability to offer more comprehensive information than data from a single view, multi-view (multi-source, multi-modal, multi-perspective, etc.) data are being used more frequently in remote sensing tasks. However, as the number of views grows, the issue of data quality becomes more apparent, limiting the potential benefits of multi-view data. Although recent deep neural network (DNN) based models can learn the weight of data adaptively, a lack of research on explicitly quantifying the data quality of each view when fusing them renders these models inexplicable, performing unsatisfactorily and inflexible in downstream remote sensing tasks. To fill this gap, in this paper, evidential deep learning is introduced to the task of aerial-ground dual-view remote sensing scene classification to model the credibility of each view. Specifically, the theory of evidence is used to calculate an uncertainty value which describes the decision-making risk of each view. Based on this uncertainty, a novel decision-level fusion strategy is proposed to ensure that the view with lower risk obtains more weight, making the classification more credible. On two well-known, publicly available datasets of aerial-ground dual-view remote sensing images, the proposed approach achieves state-of-the-art results, demonstrating its effectiveness. The code and datasets of this article are available at the following address: https://github.com/gaopiaoliang/Evidential.