Recent advances in deep neural networks (DNNs) have facilitated high-end applications, including holistic scene understanding (HSU), in which many tasks run in parallel with the same visual input. Following this trend, various methods have been proposed to use DNNs to perform multiple vision tasks. However, these methods are task-specific and less effective when considering multiple HSU tasks. End-to-end demonstrations of adversarial examples, which generate one-to-many heterogeneous adversarial examples in parallel from the same input, are scarce. Additionally, one-to-many mapping of adversarial examples for HSU usually requires joint representation learning and flexible constraints on magnitude, which can render the prevalent attack methods ineffective. In this paper, we propose PSAT-GAN, an end-to-end framework that follows the pipeline of HSU. It is based on a mixture of generative models and an adversarial classifier that employs partial weight sharing to learn a one-to-many mapping of adversarial examples in parallel, each of which is effective for its corresponding task in HSU attacks. PSAT-GAN is further enhanced by applying novel adversarial and soft-constraint losses to generate effective perturbations and avoid studying transferability. Experimental results indicate that our method is efficient in generating both universal and image-dependent adversarial examples to fool HSU tasks under either targeted or non-targeted settings.

中文翻译：

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PSAT-GAN：针对整体场景理解的有效对抗性攻击


深度神经网络 (DNN) 的最新进展促进了高端应用，包括整体场景理解 (HSU)，其中许多任务与相同的视觉输入并行运行。遵循这一趋势，人们提出了各种方法来使用 DNN 来执行多个视觉任务。然而，这些方法是特定于任务的，并且在考虑多个 HSU 任务时效率较低。对抗性示例的端到端演示很少，这些示例从同一输入并行生成一对多异构对抗性示例。此外，HSU 对抗样本的一对多映射通常需要联合表示学习和对幅度的灵活约束，这可能会使流行的攻击方法失效。在本文中，我们提出了 PSAT-GAN，这是一种遵循 HSU 流程的端到端框架。它基于生成模型和对抗性分类器的混合，该分类器采用部分权重共享来并行学习对抗性示例的一对多映射，每个模型对于 HSU 攻击中的相应任务都是有效的。通过应用新颖的对抗性和软约束损失来生成有效的扰动并避免研究可转移性，PSAT-GAN 得到了进一步增强。实验结果表明，我们的方法可以有效地生成通用和图像相关的对抗性示例，以在目标或非目标设置下愚弄 HSU 任务。