Dawn Song: Adversarial Machine Learning and Computer Security | Lex Fridman Podcast #95 | Summary and Q&A

May 12, 2020
Lex Fridman Podcast
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Dawn Song: Adversarial Machine Learning and Computer Security | Lex Fridman Podcast #95


Discover the vulnerabilities in machine learning systems, including privacy concerns and adversarial attacks, and explore potential solutions for safeguarding data ownership and protecting against attacks.

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Key Insights

  • 🔒 Privacy Vulnerabilities: The main vulnerability in privacy is the confidentiality of data in the training set used for machine learning models. Attackers can potentially extract sensitive information from the model without accessing its parameters, compromising the privacy of individuals.
  • 🌐 Data Ownership: The concept of property rights and enforcement has played a crucial role in economic growth throughout history. Applying this idea to data ownership could potentially empower individuals to have more control over their data and monetize it explicitly.
  • 🚗 Adversarial Attacks in Autonomous Driving: While it is technically feasible to carry out adversarial attacks on autonomous vehicles using vision-based sensors, the difficulty level for real-world attacks remains high. However, inherent misbehavior in these systems and the potential for targeted attacks should not be disregarded.
  • ⚖️ Protecting Privacy: Differential privacy has shown promise as a defense mechanism for protecting privacy by adding noise during the training process. This can enhance privacy protection by preventing attackers from extracting sensitive information from the learned model.
  • 🏢 Data Monetization: The current model of accessing free online services in exchange for personal data raises questions about data ownership. Exploring new models where individuals can have more control over their data and understand its value could be beneficial for both individuals and the economy.
  • 🎯 Adversarial Machine Learning: Adversarial attacks on machine learning models, both in the digital and physical world, pose significant challenges. Defending against these attacks requires strategies such as multi-modal defenses, consistency checks, and leveraging richer representations.
  • 💡 Expanding Representation Learning: Deep learning systems need to learn richer representations and capture nuanced information from the visual or sensory world, similar to human vision. This can help in building more robust and generalizable machine learning models.
  • 🔐 Ensuring Security: While progress has been made in building secure systems, vulnerabilities always exist due to the evolving nature of attacks. Ongoing research in formal verification techniques, program analysis, and innovative security measures is necessary for providing stronger security guarantees.


the following is a conversation with Dan song a professor of computer science at UC Berkeley with research interests and computer security most recently with a focus on the intersection between security and machine learning this conversation was recorded before the outbreak of the pandemic for everyone feeling the medical psychological and financia... Read More

Questions & Answers

Q: What are the main vulnerabilities in machine learning systems that require security measures?

Machine learning systems are vulnerable to both adversarial attacks, which compromise the system's integrity, and privacy breaches, which expose sensitive information in training data.

Q: How can machine learning systems be protected against adversarial attacks?

Techniques like spatial consistency checks and multi-modal defense can help enhance the resilience of machine learning systems against adversarial attacks. Additionally, formal verification and differential privacy approaches are being explored as well.

Q: What is differential privacy and how does it protect data privacy?

Differential privacy involves adding noise to the training process of machine learning systems. By doing so, it ensures that individual data points cannot be accurately inferred from the model, protecting the privacy of the training data.

Q: Why is data ownership important in the context of machine learning?

Data ownership is crucial as more personal information is generated and stored in the digital world. Recognizing and enforcing data property rights can help individuals have more control over their data and potentially drive economic growth in the digital era.


In this conversation with Dan Song, a professor of computer science at UC Berkeley, they discuss the challenges and vulnerabilities in computer security, particularly in the realm of machine learning. They explore the difficulties of creating completely bug-free code and the ever-evolving nature of attacks. They also delve into the concept of formally verified systems and the limitations of current defense mechanisms. Additionally, they touch upon the rise of attacks on humans through social engineering and the potential role of AI in protecting individuals. Lastly, they explore the field of adversarial machine learning and the creation of physical-world attacks on deep learning systems.

Questions & Answers

Q: Is it difficult to create completely bug-free code?

Yes, it is very challenging to write code that is completely free of bugs and vulnerabilities. Even with the definition of "nobility" being any type of attack that exploits code, it is nearly impossible to eliminate all vulnerabilities, especially as the nature of attacks is constantly changing. For example, in the past, memory safety vulnerabilities were a major concern, where attackers could exploit software to gain control and alter the program's state. The design intent of the program can be manipulated through buffer overflow attacks. These vulnerabilities can even lead to remote attacks, where attackers can compromise an entire program by sending malicious inputs.

Q: Can program analysis techniques provide provable guarantees of security?

Yes, program analysis and formal verification techniques can be used to prove that a piece of code has no memory safety vulnerabilities. This is known as formally verified systems, and it has been an area of focus for researchers for decades. There are already a number of formally verified systems, including microkernels, compilers, file systems, and certain crypto libraries. These systems provide verified security, but it's important to note that vulnerabilities can still exist for other types of attacks. The challenge is to continuously make progress in this space and improve the security of software systems.

Q: Can program verification techniques be performed statically?

Yes, most program verification techniques involve static analysis of the code. This means that the properties of the program are analyzed without running the code. While some techniques allow for dynamic analysis through methods like software testing and model checking, static analysis is the preferred method for program verification.

Q: Will there always be security vulnerabilities in systems?

Yes, there will likely always be security vulnerabilities in systems. Security is a complex and ever-changing field, and the diversity of attacks makes it difficult to eliminate all vulnerabilities. Even with advancements in building more secure systems and making them resilient, it is almost impossible to claim that a real-world system is 100% free of security vulnerabilities.

Q: Is there a particular security vulnerability that worries you the most?

As attacks move more and more towards targeting humans rather than systems, social engineering attacks are a major concern. Attackers manipulate and deceive humans to gain access to systems or sensitive information. For example, phishing attacks where users are tricked into providing passwords or wire money to attackers are prevalent. Additionally, the manipulation of opinions and perceptions through fake news is another worry. As these types of attacks become more severe, it is crucial to develop ways to protect users.

Q: Can machine learning help humans defend against social engineering attacks?

Yes, there are projects that utilize machine learning, specifically natural language processing (NLP) and chatbot techniques, to assist humans in defending against social engineering attacks. Chatbots can analyze conversations between users and potential attackers and detect suspicious behavior or requests, such as asking for money. They can also generate challenges and responses to test the authenticity of the correspondence. While these technologies still have room for improvement, they show promise in protecting users.

Q: Would the implementation of chatbots be at the platform level or provided as a service?

It can be implemented at both levels. While platforms like Facebook and Twitter can deploy chatbots to protect their users, it is also possible for users to employ chatbots as a service. The choice depends on the specific context and requirements.

Q: Can physical objects be manipulated to attack machine learning systems in autonomous driving?

Yes, physical objects can be used to attack machine learning systems in autonomous driving. In a research paper, examples of physical-world attacks on deep learning visual classification were explored. The idea was to manipulate a stop sign so that an image classification system would misclassify it as a different sign, potentially leading to dangerous situations. This demonstrates the vulnerability of machine learning systems in real-world environments and raises questions about their robustness.

Q: How do you design physical adversarial examples?

Designing physical adversarial examples requires careful consideration of additional constraints compared to digital attacks. In the physical world, perturbations must be added to the actual physical object, such as a stop sign, while still being perceptible by the camera that captures the input data for the machine learning system. Additionally, there are limitations when it comes to the printing and placement of these perturbations. It is a scientific process that involves optimizing the perturbations based on the objectives and constraints.

Q: What does the existence of adversarial examples reveal about neural networks?

The existence of adversarial examples highlights the limitations and challenges in deep learning systems. It showcases the need for a better understanding of how neural networks work and why they make certain predictions. It also indicates that current machine learning approaches may not be learning the right things or have rich enough representations. The connection between human vision and deep learning is essential in building more generalizable and resilient systems.

Q: Are there effective defenses against adversarial attacks?

While there are numerous defense mechanisms being developed, the current landscape favors attackers. Many defense strategies try to patch vulnerabilities or make neural networks more resilient through techniques like adversarial training. However, these approaches have limited effectiveness, and stronger and more general defenses are still lacking. One promising direction is to incorporate additional checks, such as spatial and temporal consistency, to detect adversarial examples and improve resilience.

Q: Which side is currently winning, the attackers or the defenders?

At the moment, attackers have the upper hand. Developing attacks is easier and there are many different methods and techniques available, including white-box and black-box attacks. While defense strategies do exist, there is still a long way to go in developing strong and generalizable defenses against adversarial attacks.


The conversation with Professor Dan Song highlights the challenges and vulnerabilities in computer security, particularly in the context of machine learning. The complexity and diversity of attacks make it difficult to eliminate all security vulnerabilities in systems. While progress has been made in formally verified systems and defense mechanisms, there is still work to be done. Attacks have also shifted towards targeting humans through social engineering, posing new challenges for security. Adversarial machine learning, including physical-world attacks, has revealed the limitations of current machine learning approaches and the need for richer representations. Defenses against adversarial attacks are still evolving, but promising directions include leveraging spatial and temporal consistency to improve resilience. Overall, the conversation emphasizes the ongoing efforts to enhance security and protect against evolving threats in the digital landscape.

Summary & Key Takeaways

  • Security vulnerabilities are inevitable in computer systems, including machine learning, due to the complex nature of attacks and the ever-changing landscape.

  • Formal verification techniques have been developed to prove the absence of security vulnerabilities in software, but new types of attacks continue to emerge.

  • Adversarial attacks on machine learning systems can target both inference and training stages, compromising system integrity. Solutions such as spatial consistency checks and multi-modal defense are being explored.

  • Privacy of data used in machine learning is another concern, with differential privacy techniques providing a potential solution by adding noise to the training process.

  • Ownership of data is becoming a critical issue, and recognizing and enforcing data property rights could drive economic growth in the digital era.

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