Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. Juan Carlos Lopez-Gomez, despite his U.S. citizenship and Social Security card, was arrested on April 16 on an unfounded suspicion of him being an "unauthorized alien." Immigration and Customs Enforcement kept him in county jail for 30 hours "based on biometric confirmation of his identity"--an obvious mistake of facial recognition technology. Another U.S. citizen, Jensy Machado, was held at gunpoint and handcuffed by ICE agents. He was another victim of mistaken identity after someone else gave his home address on a deportation order.

Within seconds, you've been scanned, stored, and tracked--before even reaching airport security. Without ever handing over your ID, the Transportation Security Administration (TSA) already knows exactly who you are. This is happening at 84 airports across the US. And chances are, you didn't even notice. Marketed as a tool to enhance security, TSA's facial recognition system is drawing criticism for its potential to track Americans from the terminal entrance to their final destination.

Facial recognition systems rely on embeddings to represent facial images and determine identity by verifying if the distance between embeddings is below a pre-tuned threshold. While embeddings are not reversible to original images, they still contain sensitive information, making their security critical. Traditional encryption methods like AES are limited in securely utilizing cloud computational power for distance calculations. Homomorphic Encryption, allowing calculations on encrypted data, offers a robust alternative. This paper introduces CipherFace, a homomorphic encryption-driven framework for secure cloud-based facial recognition, which we have open-sourced at http://github.com/serengil/cipherface. By leveraging FHE, CipherFace ensures the privacy of embeddings while utilizing the cloud for efficient distance computation. Furthermore, we propose a novel encrypted distance computation method for both Euclidean and Cosine distances, addressing key challenges in performing secure similarity calculations on encrypted data. We also conducted experiments with different facial recognition models, various embedding sizes, and cryptosystem configurations, demonstrating the scalability and effectiveness of CipherFace in real-world applications.

This work uses GANs to generate synthetic data to use for supervised training of facial recognition systems. More specifically, they use an image-to-image GAN to improve the quality of faces generated by a face simulator. The simulator is able to produce a wider range of face poses for a given face, and the GAN is able to refine the simulators output such that it is more closely aligned with the true distribution of faces (i.e. They show that by fine tuning a facial recognition system on this additional synthetic data they are able to improve performance and outperform previous state of the art methods. Pros: - This method is simple, apparently effective and is a nice use of GANs for a practical task.

In this paper, we propose an intelligent embedded system for monitoring class attendance and sending the attendance list to a remote computer. The proposed system consists of two parts : an embedded device (Raspberry with PI camera) for facial recognition and a web application for attendance management. The proposed solution take into account the different challenges: the limited resources of the Raspberry Pi, the need to adapt the facial recognition model and achieving acceptable performance using images provided by the Raspberry Pi camera.

A boarding system using a facial recognition technology was introduced at the Yamaman Yukarigaoka Line, a train service in a residential area in the city of Sakura, Chiba Prefecture, on Saturday. According to Yamaman, the operator of the line, it is the first time that a facial recognition-based boarding management system for general passengers has been put into practical use at a public transportation system in the country. After registering their face photo data and credit card information on a dedicated website in advance, passengers can board trains, without buying tickets, through cameras installed at the ticket gates at stations that recognize their faces. Passengers with commuter passes can also use the system. Yamaman introduced the system also to its bus service the same day.

Police and federal agencies are responding to a massive breach of personal data linked to a facial recognition scheme that was implemented in bars and clubs across Australia. The incident highlights emerging privacy concerns as AI-powered facial recognition becomes more widely used everywhere from shopping malls to sporting events. The affected company is Australia-based Outabox, which also has offices in the United States and the Philippines. In response to the Covid-19 pandemic, Outabox debuted a facial recognition kiosk that scans visitors and checks their temperature. The kiosks can also be used to identify problem gamblers who enrolled in a self-exclusion initiative.

In this paper we investigate the vulnerability that facial recognition systems present to adversarial examples by introducing a new methodology from the attacker perspective. The technique is based on the use of the autoencoder latent space, organized with principal component analysis. We intend to analyze the potential to craft adversarial examples suitable for both dodging and impersonation attacks, against state-of-the-art systems. Our initial hypothesis, which was not strongly favoured by the results, stated that it would be possible to separate between the "identity" and "facial expression" features to produce high-quality examples. Despite the findings not supporting it, the results sparked insights into adversarial examples generation and opened new research avenues in the area.

After they released a tool designed to foil facial recognition systems in 2020, computer scientist Ben Zhao and his colleagues at the University of Chicago received a confusing email. Their solution, Fawkes, subtly alters the pixels in digital portraits, rendering images incomprehensible to automated facial recognition systems. So when an artist emailed Zhao to ask whether Fawkes might be used to protect her work, he did not see the connection. Then news of revolutionary generative artificial intelligence (AI) solutions like Midjourney and Dall-E began to spread. Digital illustrations, photographs, and other visual works had been scraped from the Internet to train various generative models without the consent of the creators.

A 61-year-old man is suing Macy's and the parent company of Sunglass Hut over the stores' alleged use of a facial recognition system that misidentified him as the culprit behind an armed robbery and led to his wrongful arrest. While in jail, he was beaten and raped, according to his suit. Harvey Eugene Murphy Jr was accused and arrested on charges of robbing a Houston-area Sunglass Hut of thousands of dollars of merchandise in January 2022, though his attorneys say he was living in California at the time of the robbery. He was arrested on 20 October 2023, according to his lawyers. According to Murphy's lawsuit, an employee of EssilorLuxottica, Sunglass Hut's parent company, worked with its retail partner Macy's and used facial recognition software to identify Murphy as the robber.