AI Sec Watch

Anthropic, maker of the AI assistant Claude, is in a legal dispute with the Pentagon after being designated a supply chain risk (a company that poses a security threat to government operations). The core issue involves disagreement over whether the U.S. government can be trusted to follow the law when using AI for surveillance, given a long history of government lawyers interpreting surveillance laws in ways that expand government monitoring far beyond what the plain language of those laws seems to allow.

Current popular AI video models like Sora, Vevo, and Runway aren't very effective for making films and TV shows, despite hype suggesting AI could create entire productions automatically. AI companies are now developing custom models designed specifically for filmmakers' creative needs while trying to avoid copyright issues.

The U.S. Department of Defense designated Anthropic's Claude AI as a supply chain risk, citing concerns that the company's built-in policy preferences (established through its constitutional training approach) could compromise military effectiveness and security. The Pentagon requires defense contractors to certify they don't use Claude, though the DOD acknowledged that transitioning away from the technology will take time.

Researchers are correcting their previous work on Local Information Privacy (LIP, a method for protecting individual data when collecting information from groups of people). They discovered their original claim about how well their privacy-protecting mechanism works was not completely accurate, so they are now providing the correct range of parameters and proposing new algorithms to improve it.

This paper presents C-GAN, a method for medical image steganography (hiding secret messages inside medical images in a way that is undetectable to observers) using GANs (generative adversarial networks, a type of AI system where two neural networks compete to improve each other). The researchers improved previous steganography approaches by using a special measurement called Zero-centered Wasserstein distance to make training more stable and by adding local regularization to increase how much data can be hidden while keeping images looking natural.

Cross-silo federated learning (FL, a method where organizations train AI models together by sharing only local gradients instead of raw data) has privacy risks because gradients can leak sensitive information. FreeFL is a new approach that eliminates the need for a trusted third party and a centralized aggregator by using decentralized symmetric encryption with additive homomorphism (a type of encryption that allows computation on encrypted data), achieving better efficiency in both computation and communication than existing methods.

This research paper explores vulnerabilities in Pedestrian Attribute Recognition (PAR), a computer vision task that identifies characteristics of people in images using AI models. The authors developed both adversarial attacks (methods to fool the system with manipulated images) and a defense strategy called semantic offset defense to protect PAR systems, testing their approach on multiple datasets.

This research paper presents a new method called FoVB (Forgery-aware Audio-Visual Adaptation with Variational Bayes) to detect deepfakes (AI-generated fake videos that manipulate both audio and video). The method works by analyzing the relationship between audio and video to find mismatches, such as when lip movements don't match the sound, which are telltale signs of deepfakes.

Co-Boosting++ is a framework for one-shot federated learning (OFL, a method where multiple devices train a shared model with only one communication round), which improves how synthetic data and model ensembles work together. The framework alternates between generating challenging synthetic data samples to test the model and adjusting the ensemble weights using a Mixture of Experts mechanism (a technique that dynamically selects which component models to trust based on the task), resulting in better overall model performance.