Facial Privacy Preservation using FGSM and Universal Perturbation attacks

<p>Recent research has established the possibility of deducing soft-biometric attributes such as age, gender and race from an individual’s face image with high accuracy. Many techniques have been proposed to ensure user privacy, such as visible distortions to the images, manipulation of the original image with new face attributes, face swapping etc. Though these techniques achieve the goal of user privacy by fooling face recognition models, they don’t help the user when they want to upload original images without visible distortions or manipulation. The objective of this work is to implement techniques to ensure the privacy of user’s sensitive or personal data in face images by creating minimum pixel level distortions using white-box and black-box perturbation algorithms to fool AI models while maintaining the integrity of the image, so as to appear the same to a human eye.</p><div><br></div>

Facial Privacy Preservation using FGSM and Universal Perturbation attacks

<p>Recent research has established the possibility of deducing soft-biometric attributes such as age, gender and race from an individual’s face image with high accuracy. Many techniques have been proposed to ensure user privacy, such as visible distortions to the images, manipulation of the original image with new face attributes, face swapping etc. Though these techniques achieve the goal of user privacy by fooling face recognition models, they don’t help the user when they want to upload original images without visible distortions or manipulation. The objective of this work is to implement techniques to ensure the privacy of user’s sensitive or personal data in face images by creating minimum pixel level distortions using white-box and black-box perturbation algorithms to fool AI models while maintaining the integrity of the image, so as to appear the same to a human eye.</p><div><br></div>

Face Image Quality Assessment: A Literature Survey

The performance of face analysis and recognition systems depends on the quality of the acquired face data, which is influenced by numerous factors. Automatically assessing the quality of face data in terms of biometric utility can thus be useful to detect low-quality data and make decisions accordingly. This survey provides an overview of the face image quality assessment literature, which predominantly focuses on visible wavelength face image input. A trend towards deep learning based methods is observed, including notable conceptual differences among the recent approaches, such as the integration of quality assessment into face recognition models. Besides image selection, face image quality assessment can also be used in a variety of other application scenarios, which are discussed herein. Open issues and challenges are pointed out, i.a. highlighting the importance of comparability for algorithm evaluations, and the challenge for future work to create deep learning approaches that are interpretable in addition to providing accurate utility predictions.

Adapted discriminative coupled mappings for low-resolution face recognition with one-shot

Low-resolution face recognition with one-shot is a prevalent problem encountered in law enforcement, where it generally requires to recognize the low-resolution face images captured by surveillance cameras with the only one high-resolution profile face image in the database. The problem is very tough because the available samples is quite few and the quality of unknown images is quite low. To effectively address this issue, this paper proposes Adapted Discriminative Coupled Mappings (AdaDCM) approach, which integrates domain adaptation and discriminative learning. To achieve good domain adaptation performance for small size dataset, a new domain adaptation technique called Bidirectional Locality Matching-based Domain Adaptation (BLM-DA) is first developed. Then the proposed AdaDCM is formulated by unifying BLM-DA and discriminative coupled mappings into a single framework. AdaDCM is extensively evaluated on FERET, LFW, and SCface databases, which includes LR face images obtained in constrained, unconstrained, and real-world environment. The promising results on these datasets demonstrate the effectiveness of AdaDCM in LR face recognition with one-shot.

A Three-Dimensional Anisotropic Diffusion Equation-Based Video Recognition Model for Classroom Concentration Evaluation in English Language Teaching

In this paper, a three-dimensional anisotropic diffusion equation is used to conduct an in-depth study and analysis of students’ concentration in video recognition in English teaching classrooms. A multifeature fusion face live detection method based on diffusion model extracts Diffusion Kernel (DK) features and depth features from diffusion-processed face images, respectively. DK features provide a nonlinear description of the correlation between successive face images and express face image sequences in the temporal dimension; depth features are extracted by a pretrained depth neural network model that can express the complex nonlinear mapping relationships of images and reflect the more abstract implicit information inside face images. To improve the effectiveness of the face image features, the extracted DK features and depth features are fused using a multicore learning method to obtain the best combination and the corresponding weights. The two features complement each other, and the fused features are more discriminative, which provides a strong basis for the live determination of face images. Experiments show that the method has excellent performance and can effectively discriminate the live nature of faces in images and resist forged face attacks. Based on the above face detection and expression recognition algorithms, the classroom concentration analysis system based on expression recognition is designed to achieve real-time acquisition and processing of classroom images, complete student classroom attendance records using face detection and face recognition methods, and analyze students’ concentration from the face integrity and facial expression of students facing the blackboard by combining face detection and expression recognition to visualize and display students’ classroom data for teachers, students, and parents with more data support and help.

Research on Face Image Digital Processing and Recognition Based on Data Dimensionality Reduction Algorithm

Because face recognition is greatly affected by external environmental factors and the partial lack of face information challenges the robustness of face recognition algorithm, while the existing methods have poor robustness and low accuracy in face image recognition, this paper proposes a face image digital processing and recognition based on data dimensionality reduction algorithm. Based on the analysis of the existing data dimensionality reduction and face recognition methods, according to the face image input, feature composition, and external environmental factors, the face recognition and processing technology flow is given, and the face feature extraction method is proposed based on nonparametric subspace analysis (NSA). Finally, different methods are used to carry out comparative experiments in different face databases. The results show that the method proposed in this paper has a higher correct recognition rate than the existing methods and has an obvious effect on the XM2VTS face database. This method not only improves the shortcomings of existing methods in dealing with complex face images but also provides a certain reference for face image feature extraction and recognition in complex environment.

Prioritizing Attention over Privacy: Analysis of Face Image Posting Behavior in Online Direct-to-Consumer Genetic Testing Forum (Preprint)

BACKGROUND As direct-to-consumer genetic testing (DTC-GT) services have grown in popularity, the public has increasingly relied upon online forums to discuss and share their test results. Initially, users did so under a pseudonym, but more recently, they have included face images when discussing DTC-GT results. When these images truthfully represent a user, they reveal the identity of the corresponding individual. Various studies have shown that sharing images in social media tends to elicit more replies. However, users who do this clearly forgo their privacy. OBJECTIVE This study aimed to investigate the face image sharing behavior of DTC-GT users in an online environment and determine if there exists the association between face image sharing and the attention received from others. METHODS This study focused on r/23andme, a subreddit dedicated to discussing DTC-GT results and their implications. We applied natural language processing to infer the themes associated with posts that included a face image. We applied a regression analysis to learn the association between the attention that a post received, in terms of the number of comments and karma scores (defined as the number of upvotes minus the number of downvotes), and whether the post contains a face image. RESULTS We collected over 15,000 posts from the r/23andme subreddit published between 2012 and 2020. Face image posting began in late 2019 and grew rapidly, with over 800 individuals’ revealing their faces by early 2020. The topics in posts including a face were primarily about sharing or discussing ancestry composition, and sharing family reunion photos with relatives discovered via DTC-GT. On average, posts including a face received 60% (5/8) more comments than other posts, and these posts had karma scores 2.4 times higher than other posts. CONCLUSIONS DTC-GT consumers in the r/23andme subreddit are increasingly posting face images and testing reports on social platforms. The association between face image posting and a greater level of attention suggests that people are forgoing their privacy in exchange for attention from others. To mitigate the risk of face image posting, platforms, or at least subreddit organizers, should inform users about the consequence of such behavior for identity disclosure.