With the growth of computer vision applications, deep learning, and edge computing contribute to ensuring practical collaborative intelligence (CI) by distributing the workload among edge devices and the cloud. However, running separate single-task models on edge devices is inefficient regarding the required computational resource and time. In this context, multi-task learning allows leveraging a single deep learning model for performing multiple tasks, such as semantic segmentation and depth estimation on incoming video frames. This single processing pipeline generates common deep features that are shared among multi-task modules. However, in a collaborative intelligence scenario, generating common deep features has two major issues. First, the deep features may inadvertently contain input information exposed to the downstream modules (violating input privacy). Second, the generated universal features expose a piece of collective information than what is intended for a certain task, in which features for one task can be utilized to perform another task (violating task privacy). This paper proposes a novel deep learning-based privacy-cognizant feature generation process called MetaMorphosis that limits inference capability to specific tasks at hand. To achieve this, we propose a channel squeeze-excitation based feature metamorphosis module, Cross-SEC, to achieve distinct attention of all tasks and a de-correlation loss function with differential-privacy to train a deep learning model that produces distinct privacy-aware features as an output for the respective tasks. With extensive experimentation on four datasets consisting of diverse images related to scene understanding and facial attributes, we show that MetaMorphosis outperforms recent adversarial learning and universal feature generation methods by guaranteeing privacy requirements in an efficient way for image and video analytics.

翻译：随着计算机视觉应用的发展，深度学习与边缘计算通过将工作负载分配至边缘设备与云端，为实际协作智能的实现提供了支撑。然而，在边缘设备上运行独立的单任务模型在计算资源和时间开销方面效率低下。在此背景下，多任务学习允许利用单一深度学习模型同时执行语义分割、深度估计等多项任务，对输入视频帧进行处理。这种单一处理流水线会生成多个任务模块共享的通用深度特征。但在协作智能场景中，生成通用深度特征存在两大问题：首先，深度特征可能无意中包含暴露给下游模块的输入信息（违反输入隐私）；其次，生成的通用特征会泄露超出特定任务意图的集合信息，使得某一任务的特征可被用于执行其他任务（违反任务隐私）。本文提出一种名为MetaMorphosis的新型基于深度学习的隐私感知特征生成流程，该流程将推理能力限制于特定目标任务。为实现此目标，我们提出一种基于通道压缩-激励的特征变形模块Cross-SEC，以获取所有任务的差异化注意力，并结合差分隐私的去相关损失函数训练深度学习模型，使模型为各任务生成具有隐私感知差异性的输出特征。通过在涵盖场景理解与人脸属性的四种数据集上进行大量实验，我们证明MetaMorphosis在保障图像与视频分析隐私需求方面，以更高效的方式优于近期对抗学习与通用特征生成方法。