Face de-identification involves concealing the true identity of a face while retaining other facial characteristics. Current target-generic methods typically disentangle identity features in the latent space, using adversarial training to balance privacy and utility. However, this pattern often leads to a trade-off between privacy and utility, and the latent space remains difficult to explain. To address these issues, we propose IDeudemon, which employs a "divide and conquer" strategy to protect identity and preserve utility step by step while maintaining good explainability. In Step I, we obfuscate the 3D disentangled ID code calculated by a parametric NeRF model to protect identity. In Step II, we incorporate visual similarity assistance and train a GAN with adjusted losses to preserve image utility. Thanks to the powerful 3D prior and delicate generative designs, our approach could protect the identity naturally, produce high quality details and is robust to different poses and expressions. Extensive experiments demonstrate that the proposed IDeudemon outperforms previous state-of-the-art methods.

The widespread application of face recognition technology has exacerbated privacy threats. Face de-identification is an effective means of protecting visual privacy by concealing identity information. While deep learning-based methods have greatly improved de-identification results, most existing algorithms rely on 2D generative models that struggle to produce identity-consistent results for multiple views. In this paper, we focus on identity disentanglement within the latest 3D-aware face generation model, and propose an advanced face de-identification framework that can be applied to various scenarios. Our proposed framework disentangles identity from other facial features, modifies only the former and generates the de-identified face using a 3D generator. This approach results in high-quality, identity-consistent de-identification that preserves other facial features. We demonstrate our approach on StyleNeRF, one of the most widely-used style-based neural radiation field models. Through extensive experiments, we demonstrate the effectiveness of our approach in achieving face de-identification both for a single image and group images with the same identity. Our work is a significant step forward in the field of face de-identification, opening up new possibilities for practical applications.

Unprecedented video collection and sharing have exacerbated privacy concerns and led to increasing interest in privacy-preserving tools. A satisfactory video de-identification tool should be able to remove sensitive identity information from face videos while maintaining useful information for other identity-agnostic tasks. Meanwhile, it is necessary to allow the authority to inspect real identity when abnormal events are detected. Existing methods only focus on the study of de-identification, and lack the desired recovery ability when granting permissions. Furthermore, they all process the videos frame by frame, which hardly benefit from motion and inter-frame information. In this paper, we propose a modular architecture for reversible face video de-identification, called IdentityMask, which leverages deep motion flow to avoid per-frame evaluation. Our framework consists of two processes: the de-identification process provides a protective mask for identity information, while the recovery process can remove the protective mask if and only if the right key is provided. To this end, a Protection Module and a Recovery Module are built as two major functional modules, both based on an identity disentanglement network and guided by a crucial Motion Flow Module. An Affine Transformation Module provides simple but reliable assistance. Extensive experiments on a diverse natural video dataset (gender, ethnicity, age, etc.) demonstrate the effectiveness of the proposed framework for reversible face video de-identification.

The popularization of intelligent devices including smartphones and surveillance cameras results in more serious privacy issues. De-identification is regarded as an effective tool for visual privacy protection with the process of concealing or replacing identity information. Most of the existing de-identification methods suffer from some limitations since they mainly focus on the protection process and are usually non-reversible. In this paper, we propose a personalized and invertible de-identification method based on the deep generative model, where the main idea is introducing a user-specific password and an adjustable parameter to control the direction and degree of identity variation. Extensive experiments demonstrate the effectiveness and generalization of our proposed framework for both face de-identification and recovery.

Advances in cameras and web technology have made it easy to capture and share large amounts of face videos over to an unknown audience with uncontrollable purposes. These raise increasing concerns about unwanted identity-relevant computer vision devices invading the characters's privacy. Previous de-identification methods rely on designing novel neural networks and processing face videos frame by frame, which ignore the data feature in redundancy and continuity. Besides, these techniques are incapable of well-balancing privacy and utility, and per-frame evaluation is easy to cause flicker. In this paper, we present deep motion flow, which can create remarkable de-identified face videos with a good privacy-utility tradeoff. It calculates the relative dense motion flow between every two adjacent original frames and runs the high quality image anonymization only on the first frame. The de-identified video will be obtained based on the anonymous first frame via the relative dense motion flow. Extensive experiments demonstrate the effectiveness of our proposed de-identification method.

As more and more personal photos are shared and tagged in social media, security and privacy protection are becoming an unprecedentedly focus of attention. Avoiding privacy risks such as unintended verification, becomes increasingly challenging. To enable people to enjoy uploading photos without having to consider these privacy concerns, it is crucial to study techniques that allow individuals to limit the identity information leaked in visual data. In this paper, we propose a novel hybrid model consists of two stages to generate visually pleasing de-identified face images according to a single input. Meanwhile, we successfully preserve visual similarity with the original face to retain data usability. Our approach combines latest advances in GAN-based face generation with well-designed adjustable randomness. In our experiments we show visually pleasing de-identified output of our method while preserving a high similarity to the original image content. Moreover, our method adapts well to the verificator of unknown structure, which further improves the practical value in our real life.