Adversarial patches are images designed to fool otherwise well-performing neural network-based computer vision models. Although these attacks were initially conceived of and studied digitally, in that the raw pixel values of the image were perturbed, recent work has demonstrated that these attacks can successfully transfer to the physical world. This can be accomplished by printing out the patch and adding it into scenes of newly captured images or video footage.

CSET Senior Fellow Andrew Lohn testified before the House of Representatives Homeland Security Subcommittee on Cybersecurity, Infrastructure Protection, and Innovation at a hearing on "Securing the Future: Harnessing the Potential of Emerging Technologies While Mitigating Security Risks." Lohn discussed the application of AI systems in cybersecurity and AI’s vulnerabilities.

China’s State Key Laboratory system drives innovation in science and technology. These labs conduct cutting-edge basic and applied research, attract and train domestic and foreign talent, and conduct academic exchanges with foreign counterparts. This report assesses trends in the research priorities, management structures, and talent recruitment efforts of nearly five hundred Chinese State Key Labs. The accompanying data visualization maps their geographical locations and host institutions.

CSET Senior Fellow Andrew Lohn testified before the House of Representatives Science, Space and Technology Subcommittee on Investigations and Oversight and Subcommittee on Research and Technology at a hearing on "Securing the Digital Commons: Open-Source Software Cybersecurity." Lohn discussed how the United States can maximize sharing within the artificial intelligence community while reducing risks to the AI supply chain.

CSET Senior Fellow Andrew Lohn testified before the U.S. Senate Armed Services Subcommittee on Cybersecurity hearing on artificial intelligence applications to operations in cyberspace. Lohn discussed AI's capabilities and vulnerabilities in cyber defenses and offenses.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This is the fourth in a series of Snapshots exploring CSET’s Private-sector AI-Related Activity Tracker (PARAT). Check in every two weeks to see our newest Snapshot, and explore PARAT, which collects data related to companies’ AI research and development to inform analysis of the global AI sector.

Like traditional software, vulnerabilities in machine learning software can lead to sabotage or information leakages. Also like traditional software, sharing information about vulnerabilities helps defenders protect their systems and helps attackers exploit them. This brief examines some of the key differences between vulnerabilities in traditional and machine learning systems and how those differences can affect the vulnerability disclosure and remediation processes.

This brief illuminates the scale of Chinese government funding for higher education, science, and technology by exploring budget and expense reports for key government organizations and 34 of China’s most elite “Double First Class” universities. Chinese political leaders view elite universities as key components of the country’s military modernization, economic growth, and soft power; a situation that presents security risks for international partners.

CSET Research Analyst Dakota Cary testified before the U.S.-China Economic and Security Review Commission hearing on "China’s Cyber Capabilities: Warfare, Espionage, and Implications for the United States." Cary discussed the cooperative relationship between Chinese universities and China’s military and intelligence services to develop talent with the capabilities to perform state-sponsored cyberespionage operations.

Between 2012 and 2018, the amount of computing power used by record-breaking artificial intelligence models doubled every 3.4 months. Even with money pouring into the AI field, this trendline is unsustainable. Because of cost, hardware availability and engineering difficulties, the next decade of AI can't rely exclusively on applying more and more computing power to drive further progress.