’90s sci-fi movies are full of computers showing this rotating profile of a person and displaying all sorts of information about the person. This facial recognition technology is expected to be so advanced that no data about you can remain hidden from the big brother.
Unfortunately, we cannot say that they were wrong. Facial recognition technology has witnessed significant advances with the advent of deep learning-based systems, which have revolutionized various applications and industries. Whether this revolution was a good thing or a bad thing is a topic for another post, but the reality is that our faces can be linked to a lot of data about us in our world. In this case, privacy plays a crucial role.
In response to these concerns, the research community has been actively exploring methods and techniques to develop facial privacy protection algorithms that can protect individuals against potential risks associated with facial recognition systems.
The goal of facial privacy protection algorithms is to find a balance between preserving an individual’s privacy and maintaining the usability of their facial images. While the primary goal is to protect individuals from unauthorized identification or tracking, it is equally important to ensure that protected images retain visual fidelity and resemblance to the original faces so that the system cannot be fooled with a fake face. .
Achieving this balance is challenging, especially when using noise-based methods that superimpose adversarial artifacts on the original facial image. Several approaches have been proposed to generate contradictory examples without constraints, with contradictory makeup-based methods being the most popular for their ability to incorporate contradictory modifications in a more natural way. However, existing techniques have limitations, such as makeup artifacts, dependency on reference images, the need to retrain for each target identity, and a focus on spoofing rather than privacy preservation.
Therefore, there is a need for a reliable method to protect facial privacy, but the existing ones have obvious shortcomings. How can we solve this? time to meet CLIP2 Protection.
CLIP2 Protection is a novel approach to protect user’s facial privacy on online platforms. It involves searching for adversarial latent codes in a low-dimensional manifold learned by a generative model. These latent codes can be used to generate high-quality facial images that maintain realistic facial identity while fooling black box FR systems.
A key component of CLIP2 Protection he is using textual cues to facilitate adversarial makeup transfer, allowing one to traverse the latent manifold of the generative model to find transferable adversarial latent codes. This technique effectively hides attack information within the desired makeup style without requiring large makeup data sets or retraining for different target identities. CLIP2 Protection it also introduces an identity-preserving regularization technique to ensure that protected face images visually resemble the original faces.
To ensure the naturalness and fidelity of protected images, adversarial face search is forced to stay close to the range of clean images learned by the generative model. This restriction helps mitigate the generation of artifacts or unrealistic features that could be easily detected by human observers or automated systems. Besides, CLIP2 Protection focuses on optimizing only identity-preserving latent codes in latent space, ensuring that protected faces retain the individual’s perceived human identity.
To introduce privacy-enhancing disturbances, CLIP2 Protection Use text prompts as a guide to generate make-up-like transformations. This approach offers greater flexibility to the user than methods based on reference images, as it allows the desired makeup styles and attributes to be specified through textual descriptions. By leveraging these textual cues, the method can effectively incorporate privacy protection information into the makeup style without the need for a large makeup data set or retraining for different target identities.
Extensive experiments are carried out to evaluate the efficacy of the CLIP2 Protection in face verification and identification scenarios. The results demonstrate its effectiveness against black box FR models and commercial online facial recognition APIs.
review the Paper and project page. Don’t forget to join our 25k+ ML SubReddit, discord channel, and electronic newsletter, where we share the latest AI research news, exciting AI projects, and more. If you have any questions about the article above or if we missed anything, feel free to email us at [email protected]
🚀 Check out 100 AI tools at AI Tools Club
Ekrem Çetinkaya received his B.Sc. in 2018, and M.Sc. in 2019 from Ozyegin University, Istanbul, Türkiye. She wrote her M.Sc. thesis on denoising images using deep convolutional networks. She received her Ph.D. He graduated in 2023 from the University of Klagenfurt, Austria, with his dissertation titled “Video Coding Improvements for HTTP Adaptive Streaming Using Machine Learning”. His research interests include deep learning, computer vision, video encoding, and multimedia networking.
