Topic

DeepSeek R1 Technical Replication and the Governance and Security of Large Models

With the rapid development of inference big model technology represented by DeepSeek R1, AI has made breakthroughs in inference efficiency, multimodal understanding, etc., but its security and governance issues have also triggered global concern. The complexity of the big model technology may lead to potential vulnerabilities being overlooked, while issues such as the low authenticity of the generated content and the lack of defense against malicious attacks directly affect the reliability of the technology on the ground. For example, although the efficient inference capability of DeepSeek R1 empowers intelligent customer service, code generation and other scenarios, its performance in data privacy protection and robustness against adversarial attacks still needs systematic verification. Our team focuses on DeepSeek R1's technical reproduction and security governance research: on the one hand, through the open source code reproduction and optimization, in-depth analysis of the potential risks of the model architecture; on the other hand, for the security of the large model itself, using red team attacks to simulate the tip injection, data poisoning and other attack modes, to build a vulnerability detection and defense scheme; at the same time, combined with multimodal feature extraction and adversarial training At the same time, combined with multimodal feature extraction and adversarial training technology, we develop in-depth forensic algorithms covering text, images, and videos to curb the dissemination of false information from the source. These efforts not only provide a security baseline for the technical optimization of DeepSeek R1 and other language inference models, but also build a trustworthy environment for the industrial application of these models, helping to realize a safe and controllable intelligent future. Outline: This speech takes “DeepSeek R1 technology reproduction and the governance and security of large models” as the topic, first introduces itself and the theme, and introduces the topic of AI security governance through the security incidents caused by AI, such as false information dissemination and model privacy leakage; then lists the examples of improving efficiency and changing business models in various fields to show the current situation of AI development; then elaborates on the development of DeepSeek R1 and other large models. The current situation of AI development; then elaborates that DeepSeek R1 faces model risks such as prompt injection, data poisoning, and the dissemination of false content that threatens information security and social order; then introduces the team's research work of simulating the excavation of model loopholes using red team attacks, and detecting false content using multimodal technology combined with image and language processing algorithms; and describes the significance of these achievements for guaranteeing the stability of the AI system, promoting the application of the industry, and safeguarding the social and economic order. Finally, we summarize the hidden dangers of AI security and the team's research results, look forward to the future of security, and call for joint efforts to promote the sustainable development of AI.