No abstract

Video surveillance is increasingly omnipresent in our everyday life and is a key component of many security systems. Not only is the increasing number of cameras, but also the resolution of visual sensors and the performance of video processing algorithms. This evolution generates some important privacy concerns. This article introduces a new visual filter that includes a good trade-off between privacy and intelligibility. It ensures that people are unrecognizable while keeping the scene understandable in terms of events which allows machines to detect abnormal behavior. The algorithm operates in the DCT domain to be compliant with the popular JPEG and MPEG codecs. For each sensitive area of the picture (i.e. area where privacy needs to be protected), the proposed algorithm uses the low-frequency coefficients of the DCT to display a privacy preserved image of the region and the high-frequency coefficients to hide most of the original information. Finally, our process allows authorized users to nearly reverse the process thanks to the hidden information.

Here, the authors propose a scalable scrambling algorithm operating in the discrete cosine transform (DCT) domain within the JPEG codec. The goal is to ensure that people are no more identifiable while keeping their actions still understandable regardless of the image size. For each 8 × 8 block, the authors encrypt the DCT coefficients to protect data information, and shift them towards the high frequencies to make the DC position available. Whereas encrypted coefficients appear as noise in the protected image, the DC position is dedicated to restitute some of the original information (e.g. the average colour associated with one or a group of blocks). The proposed approach automatically sets the value of each DC according to the region of interest size in order to keep the level of privacy protection strong enough. Comparing to existing methods, the proposed privacy protection framework provides flexibility concerning the appearance of the protected version which makes it stronger for protecting the privacy even during potential attacks. Moreover, the method does not cause excessive perturbation for the recognition of the actions and slightly decreases the efficiency of the JPEG standard.

Deep learning-based algorithms have become increasingly efficient in recognition and detection tasks, especially when they are trained on large-scale datasets. Such recent success has led to a speculation that deep learning methods are comparable to or even outperform human visual system in its ability to detect and recognize objects and their features. In this paper, we focus on the specific task of gender recognition in images when they have been processed by privacy protection filters (e.g., blurring, masking, and pixelization) applied at different strengths. Assuming a privacy protection scenario, we compare the performance of state of the art deep learning algorithms with a subjective evaluation obtained via crowdsourcing to understand how privacy protection filters affect both machine and human vision.

The evolution of the video surveillance systems generates questions concerning protection of individual privacy. In this paper, we design ASePPI, an Adaptive Scrambling enabling Privacy Protection and Intelligibility method operating in the H.264/AVC stream with the aim to be robust against de-anonymization attacks targeting the restoration of the original image and the re-identification of people. The proposed approach automatically adapts the level of protection according to the resolution of the region of interest. Compared to existing methods, our framework provides a better trade-off between the privacy protection and the visibility of the scene with robustness against deanonymization attacks. Moreover, the impact on the source coding stream is negligible.