Event Encryption for Neuromorphic Vision Sensors: Framework, Algorithm, and Evaluation

Abstract

Nowadays, our lives have benefited from various vision-based applications, such as video surveillance, human identification and aided driving. Unauthorized access to the vision-related data greatly threatens users’ privacy, and many encryption schemes have been proposed to secure images and videos in those conventional scenarios. Neuromorphic vision sensor (NVS) is a brand new kind of bio-inspired sensor that can generate a stream of impulse-like events rather than synchronized image frames, which reduces the sensor’s latency and broadens the applications in surveillance and identification. However, the privacy issue related to NVS remains a significant challenge. For example, some image reconstruction and human identification approaches may expose privacy-related information from NVS events. This work is the first to investigate the privacy of NVS. We firstly analyze the possible security attacks to NVS, including grayscale image reconstruction and privacy-related classification. We then propose a dedicated encryption framework for NVS, which incorporates a 2D chaotic mapping to scramble the positions of events and flip their polarities. In addition, an updating score has been designed for controlling the frequency of execution, which supports efficient encryption on different platforms. Finally, extensive experiments have demonstrated that the proposed encryption framework can effectively protect NVS events against grayscale image reconstruction and human identification, and meanwhile, achieve high efficiency on various platforms including resource-constrained devices.

Event encryption: rethinking privacy exposure for neuromorphic imaging

“…Compressive sensing [18] that is primarily used for data shrinking, also contributes to low-cost security enhancements [19]. Target to neuromorphic imaging, a recent proposal bridged traditional chaotic schemes with polarity flipping [3]. This work continues the discussion on event encryption from a new perspective-neuromorphic noise removal.…”

“…One can use advanced techniques to perform high-level reasoning on event streams and then efficiently acquire privacy-relevant information. Evaluated on N-MNIST [25] and ASL-DVS [26], table 1 presents the top-1 recognition accuracy on raw events, on raw events with 50% random noise, on the encrypted events by the competitor (Du et al [3]) and on our encrypted events. The approaches, which are either grid-based [13,29,33] or graph-based [32], can still have good performance when the data are filled with random noise, but they fail for recognition on the encrypted ones.…”

“…Neuromorphic privacy challenges were first raised in a recent study [3]. In this work, we discuss more attack scenarios and approach event encryption from another perspective-discrimination between events and noise.…”

“…Inspired by frame-based image encryption, Du et al [3] conducted the first investigation on this issue through incorporating two-dimensional chaotic mapping and a key updating mechanism. Compared with the counterpart, our technique enables direct encryption of four-dimensional event streams in an unsupervised manner, and also requires fewer operations for lossless decryption.…”

Adaptive Slicing Method of the Spatiotemporal Event Stream Obtained from a Dynamic Vision Sensor

“…As a result of their unique pixel structure, these sensors only respond where the light intensity changes, and have the advantages of high dynamic range, low data volume, and low power consumption [ 5 ]. Hence, dynamic vision sensors have been gradually applied to object tracking [ 6 , 7 , 8 ], surveillance and monitoring [ 9 , 10 , 11 , 12 , 13 ], star tracking [ 14 ], etc.…”