Resemble AI has launched DETECT-3B Omni, a deepfake detection model that analyzes audio, images, and video using a unified system. It boasts enhanced capabilities over its predecessor, DETECT-2B, including expanded training data, support for over 40 languages, and protections against modern threats like replay attacks. The model ranks highly on various benchmarks for its detection accuracy across multiple media types.

A recent study found that over 90% of participants could not reliably distinguish between real and AI-generated videos. The findings highlight the impressive advancements in AI video generation, particularly with the Gen-4.5 model, and raise concerns about the implications for video authenticity and trust.

This article explores how AI agents, specifically Claude Code, streamline the threat hunting process in security operations. Using Model Context Protocol (MCP) servers, analysts can quickly gather evidence and prioritize threats for investigation, transforming a traditionally manual task into a more efficient workflow.

Google has updated its Gemini app to allow users to verify if videos were created by its AI. By uploading a video, users can check for a digital watermark that indicates AI involvement. However, this tool only works for content generated by Google's own systems.

Vega offers a solution for security operations without the need for data migration or complex setups. Its AI-powered analytics and detection provide immediate visibility across all data, enabling faster and more effective security responses. You maintain control over your data while benefiting from rapid onboarding.

Resemble AI has launched DETECT-3B Omni, a deepfake detection model that analyzes audio, images, and video through a single API. It improves upon its predecessor with expanded training data, increased language support, and enhanced protection against modern synthetic media threats. The model achieves top performance benchmarks across all modalities.

This article discusses the risks of prompt injection attacks on AI browser agents and presents a benchmark for evaluating detection mechanisms. It highlights the challenges in creating effective security systems and introduces a fine-tuned model that improves attack detection while maintaining user experience.

The article discusses advancements in artificial intelligence aimed at defending against deepfake technology, which poses significant risks to personal and organizational security. It emphasizes the importance of developing robust detection methods to identify manipulated media and protect against misinformation. Additionally, the piece highlights the need for ongoing research and collaboration in this evolving field.

The "am-i-vibing" library detects whether CLI tools and Node applications are being executed by AI agents, allowing them to adjust outputs and error handling accordingly. It provides functions for detecting different types of environments—agentic, interactive, and hybrid—and can be used via CLI for quick checks and detailed diagnostics.