


Behind the rapid development of artificial intelligence there are multiple security risks
Imagine that if someone puts a "sticker" on their face, the face recognition access control system can mistake it for you and open the door easily; if you put the same "sticker" on your glasses, You can unlock your phone's face recognition in just one second and explore your privacy as if you were in an uninhabited land. This is not the imagination of a science fiction blockbuster, but a real attack and defense scene displayed at the award ceremony of the first Artificial Intelligence Security Competition.
Not long ago, the first Artificial Intelligence Security Conference was jointly sponsored by the National Industrial Information Security Development Research Center, Tsinghua University Artificial Intelligence Research Institute and Beijing Ruilai Intelligent Technology Co., Ltd. The competition ends. During the competition, discussions arose about the security risks of artificial intelligence. Experts attending the meeting said that artificial intelligence security risks are no longer future challenges, but immediate threats. We must pay attention to the construction of artificial intelligence security systems and accelerate the promotion of key technology research and offensive and defensive practices in the field of artificial intelligence security.
Artificial intelligence, like other general technologies, is making rapid progress, but it also brings certain risks and hidden dangers. Tian Tian, CEO of Ruilai Smart, who has won the "Wu Wenjun Artificial Intelligence Outstanding Youth Award", believes that the scope of artificial intelligence technology risks is gradually expanding as the application scenarios become more widespread, and the possibility of risks also increases with its application scenarios. It continues to increase with the increase in application frequency. In his view, the current security risks of artificial intelligence can be analyzed mainly from the two perspectives of "people" and "systems".
Assessing the security issues of artificial intelligence from a human perspective, the first thing to bear the brunt is the duality of technology, and the problem of abuse of artificial intelligence. Specific to the application of artificial intelligence, the most typical representative is deepfake technology, whose negative application risks continue to intensify and have caused substantial harm.
The facial recognition cracking demonstration in this competition reveals the risks of the system, which come from the fragility of the deep learning algorithm itself. The second generation of artificial intelligence with deep learning algorithms as the core is a "black box" and is unexplainable, which means that the system has structural loopholes and may be subject to unpredictable risks. A typical example is the "magic sticker" on-site demonstration. , is actually an "adversarial sample attack", which causes the system to make wrong judgments by adding disturbances to the input data.
This vulnerability also exists in the autonomous driving perception system. Under normal circumstances, after identifying roadblocks, signs, pedestrians and other targets, the self-driving vehicle will stop immediately. However, after adding interference patterns to the target objects, the vehicle's perception system may make errors and directly crash into them.
During the competition, the "White Paper on Security Development of Artificial Intelligence Computing Infrastructure" was released. It is mentioned that artificial intelligence computing power infrastructure is different from traditional computing power infrastructure. It is both "infrastructure", "artificial intelligence computing power" and "public facilities", and has the triple attributes of infrastructure, technology and public attributes. Accordingly, promoting the safe development of artificial intelligence computing power infrastructure should focus on strengthening its own security, ensuring operational safety, and assisting safety compliance.
Coordinating development and security seems to be an inevitable problem faced in the development process of every new technology. How to achieve a positive interaction between high-level development and high-level security is also one of the most important propositions in the current development of the artificial intelligence industry. , many experts on site discussed this topic.
"Artificial intelligence adversarial attack and defense include adversarial samples, neural network backdoors, model privacy issues and other technologies. If the model has errors, it needs to be repaired in a timely manner." Chen, deputy director of the State Key Laboratory of Information Security, Chinese Academy of Sciences Kai proposed a "neural network scalpel" method to perform precise "minimally invasive" repairs by locating the neurons that caused the error.
Chen Kai said that unlike traditional model repair work, which requires retraining the model or relies on a larger number of data samples, this method is similar to "minimally invasive surgery" and only requires a very small amount of data samples. Greatly improve the model repair effect.
Artificial intelligence systems in open environments face many security challenges. How to solve the security issue of the full cycle of general artificial intelligence algorithms has become a top priority.
Liu Xianglong, deputy director of the State Key Laboratory of Software Development Environment at Beihang University, said that from a technical point of view, a complete technical means from security testing to security analysis and security reinforcement should be formed, and finally a standardized test should be formed process.
He also pointed out that future artificial intelligence security should focus on comprehensive evaluation at all levels from data, algorithms to systems, and at the same time cooperate with a set of safe and trusted computing environments from hardware to software.
Su Jianming, an expert in charge of the Security Offense and Defense Laboratory of the Industrial and Commercial Bank of China Financial Research Institute, said that artificial intelligence security governance requires extensive collaboration and open innovation, and it is necessary to strengthen the interaction and cooperation of various industry participants such as governments, academic institutions, enterprises, etc., to establish a positive ecosystem rule. At the policy level, the legislative process of artificial intelligence should be accelerated, and special supervision and assessment of artificial intelligence service levels and technical support capabilities should be strengthened. At the academic level, increase incentives for artificial intelligence safety research and accelerate the transformation and implementation of scientific research results through the industry-university-research cooperation model. At the enterprise level, we will gradually promote the transformation of artificial intelligence technology from scenario expansion to safe and trustworthy development, and continue to explore artificial intelligence safety practices and solutions by participating in the formulation of standards, launching products and services.
In fact, building a safe ecosystem for artificial intelligence requires the continuous evolution of technology on the one hand, and the construction and training of specialized technical talents on the other. Tian Tian said that because artificial intelligence security research is still an emerging field, there are few specialized talents, and there is a lack of systematic research teams. This competition uses actual combat exercises to verify and improve the actual combat capabilities of the players, in order to cultivate a group of high-level, The high-level artificial intelligence security new talent team provides a "fast track".
Experts believe that in the long run, the security issues of artificial intelligence need to be broken through from the principles of algorithm models. Only by continuing to strengthen basic research can core scientific issues be solved. At the same time, they emphasized that the security of artificial intelligence Future development needs to ensure the effectiveness and positive promotion of the development of the entire society and the country, and requires the coordinated development of multiple parties including government, industry, academia, and research.
The above is the detailed content of Behind the rapid development of artificial intelligence there are multiple security risks. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics



This site reported on June 27 that Jianying is a video editing software developed by FaceMeng Technology, a subsidiary of ByteDance. It relies on the Douyin platform and basically produces short video content for users of the platform. It is compatible with iOS, Android, and Windows. , MacOS and other operating systems. Jianying officially announced the upgrade of its membership system and launched a new SVIP, which includes a variety of AI black technologies, such as intelligent translation, intelligent highlighting, intelligent packaging, digital human synthesis, etc. In terms of price, the monthly fee for clipping SVIP is 79 yuan, the annual fee is 599 yuan (note on this site: equivalent to 49.9 yuan per month), the continuous monthly subscription is 59 yuan per month, and the continuous annual subscription is 499 yuan per year (equivalent to 41.6 yuan per month) . In addition, the cut official also stated that in order to improve the user experience, those who have subscribed to the original VIP

Improve developer productivity, efficiency, and accuracy by incorporating retrieval-enhanced generation and semantic memory into AI coding assistants. Translated from EnhancingAICodingAssistantswithContextUsingRAGandSEM-RAG, author JanakiramMSV. While basic AI programming assistants are naturally helpful, they often fail to provide the most relevant and correct code suggestions because they rely on a general understanding of the software language and the most common patterns of writing software. The code generated by these coding assistants is suitable for solving the problems they are responsible for solving, but often does not conform to the coding standards, conventions and styles of the individual teams. This often results in suggestions that need to be modified or refined in order for the code to be accepted into the application

Large Language Models (LLMs) are trained on huge text databases, where they acquire large amounts of real-world knowledge. This knowledge is embedded into their parameters and can then be used when needed. The knowledge of these models is "reified" at the end of training. At the end of pre-training, the model actually stops learning. Align or fine-tune the model to learn how to leverage this knowledge and respond more naturally to user questions. But sometimes model knowledge is not enough, and although the model can access external content through RAG, it is considered beneficial to adapt the model to new domains through fine-tuning. This fine-tuning is performed using input from human annotators or other LLM creations, where the model encounters additional real-world knowledge and integrates it

To learn more about AIGC, please visit: 51CTOAI.x Community https://www.51cto.com/aigc/Translator|Jingyan Reviewer|Chonglou is different from the traditional question bank that can be seen everywhere on the Internet. These questions It requires thinking outside the box. Large Language Models (LLMs) are increasingly important in the fields of data science, generative artificial intelligence (GenAI), and artificial intelligence. These complex algorithms enhance human skills and drive efficiency and innovation in many industries, becoming the key for companies to remain competitive. LLM has a wide range of applications. It can be used in fields such as natural language processing, text generation, speech recognition and recommendation systems. By learning from large amounts of data, LLM is able to generate text

Machine learning is an important branch of artificial intelligence that gives computers the ability to learn from data and improve their capabilities without being explicitly programmed. Machine learning has a wide range of applications in various fields, from image recognition and natural language processing to recommendation systems and fraud detection, and it is changing the way we live. There are many different methods and theories in the field of machine learning, among which the five most influential methods are called the "Five Schools of Machine Learning". The five major schools are the symbolic school, the connectionist school, the evolutionary school, the Bayesian school and the analogy school. 1. Symbolism, also known as symbolism, emphasizes the use of symbols for logical reasoning and expression of knowledge. This school of thought believes that learning is a process of reverse deduction, through existing

Editor |ScienceAI Question Answering (QA) data set plays a vital role in promoting natural language processing (NLP) research. High-quality QA data sets can not only be used to fine-tune models, but also effectively evaluate the capabilities of large language models (LLM), especially the ability to understand and reason about scientific knowledge. Although there are currently many scientific QA data sets covering medicine, chemistry, biology and other fields, these data sets still have some shortcomings. First, the data form is relatively simple, most of which are multiple-choice questions. They are easy to evaluate, but limit the model's answer selection range and cannot fully test the model's ability to answer scientific questions. In contrast, open-ended Q&A

Editor | KX In the field of drug research and development, accurately and effectively predicting the binding affinity of proteins and ligands is crucial for drug screening and optimization. However, current studies do not take into account the important role of molecular surface information in protein-ligand interactions. Based on this, researchers from Xiamen University proposed a novel multi-modal feature extraction (MFE) framework, which for the first time combines information on protein surface, 3D structure and sequence, and uses a cross-attention mechanism to compare different modalities. feature alignment. Experimental results demonstrate that this method achieves state-of-the-art performance in predicting protein-ligand binding affinities. Furthermore, ablation studies demonstrate the effectiveness and necessity of protein surface information and multimodal feature alignment within this framework. Related research begins with "S

According to news from this site on August 1, SK Hynix released a blog post today (August 1), announcing that it will attend the Global Semiconductor Memory Summit FMS2024 to be held in Santa Clara, California, USA from August 6 to 8, showcasing many new technologies. generation product. Introduction to the Future Memory and Storage Summit (FutureMemoryandStorage), formerly the Flash Memory Summit (FlashMemorySummit) mainly for NAND suppliers, in the context of increasing attention to artificial intelligence technology, this year was renamed the Future Memory and Storage Summit (FutureMemoryandStorage) to invite DRAM and storage vendors and many more players. New product SK hynix launched last year
