Adversarial machine learning highlights a paradox, it reveals how models can be both exceptional and vulnerable. As a serial entrepreneur deeply involved in deeptech and artificial intelligence, I’ve learned that understanding features, even when mislabeled or manipulated, can produce actionable insights for training robust, real-world systems.
In 2019, Andrew Ilyas and his team published "Adversarial Examples Are Not Bugs, They Are Features," a paper discussing how adversarial examples expose not errors in the data but features of the model itself. This concept fascinated me in my journey of building artificial intelligence tools, particularly in applications where misclassified data might still hold value.
Here’s what the research explores: neural networks don’t merely misidentify adversarial examples because of random errors. They do so because these examples exploit non-robust features, patterns that humans overlook but algorithms consistently detect. Let’s break down why this matters for anyone building AI systems.
Understanding Adversarial Examples
Adversarial examples are inputs deliberately tweaked to confuse a model. For instance, an image classified as a cat can be adjusted with minor pixel changes, imperceptible to humans, leading a model to suddenly classify it as a dog. Models don’t misclassify at random, they over-rely on subtle data distributions invisible to the human eye.
The key takeaway is that these adversarial inputs aren’t flaws in the system. They exploit features in the dataset that are both brittle and predictive but not aligned with human intuition. This distinction reframes adversarial vulnerability as a feature-inference problem.
What Ilyas et al. Did Differently
One of the most intriguing aspects of the paper was their experiment with mislabeled data. The research team used adversarial models to relabel training datasets incorrectly, essentially flipping the true label to the adversarially predicted one. Surprisingly, a model retrained on these mislabeled datasets achieved solid accuracy on clean test data.
For example:
- A ResNet-18 model trained only on adversarial examples from CIFAR-10 images achieved roughly 50% accuracy on clean test data.
- When conducting the same experiment with out-of-domain data, such as repurposing Fashion-MNIST images to unintuitive labels (like dresses labeled as “three”), the model still generalized well to the original task.
In plain terms, even when learning from garbage labels, models could abstract useful, transferable patterns. This insight has significant implications for entrepreneurs building AI designed to operate in messy, real-world conditions.
Practical Applications for Entrepreneurs
If you’re involved in a startup leveraging machine learning, here’s why these findings might reshape your approach:
-
Rethink Generalization
Machine learning models don’t just learn direct mappings between input and output. They abstract over underlying patterns, even when the labels are off. If you're working with incomplete or noisy datasets, this insight could encourage you to leverage them creatively. -
Improve Data Augmentation
Techniques like adversarial training are often used to enhance model robustness. This study highlights how retraining with mislabeled outputs isn’t necessarily catastrophic. It could even reinforce data diversity, making your models more adaptive. -
Lower Dependence on Clean Data
Startups often struggle with acquiring perfectly labeled datasets. Knowing that incorrectly labeled or adversarial data can still train effective systems may save both time and costs. Experimentation with error-type data shouldn’t feel risky, it could uncover the very features that differentiate you from competitors.
How to Apply These Lessons in Real Life
Let me offer a quick guide to incorporating this thinking into your AI product strategy:
1. Test Using Adversarial Data
Create adversarial datasets from your existing models. Tools like Foolbox can help you generate adversarial examples. Run these through your primary model, then observe its decisions. This step identifies vulnerable patterns and builds resilience.
2. Retrain on Mislabeled Data
After generating adversarial data, consider retraining using incorrect labels predicted by the adversarial model. Evaluate performance on a clean validation set. The improvement may surprise you, proving that integrating features from errors strengthens predictive power.
3. Monitor Real-World Drift
In production systems, monitor for data drift, the gradual change in dataset properties over time. Leverage adversarial examples to simulate shifts and preemptively adapt your model.
Common Mistakes to Avoid
Even the best intentions around adversarial training can backfire if not executed correctly. Here are crucial pitfalls to watch out for:
Assuming All Error Data Is Useful
Not all mislabeled datasets will improve your model. The usefulness depends on whether the mislabeled patterns retain structural qualities relevant to the task.
Ignoring Human Intuition
It’s tempting to trust the model’s ability to harness features from errors, but combining automated insights with human judgment will always produce better results. You can’t fully automate testing these principles.
Forgetting Model Behavior Explainability
Retraining models on incomprehensible features risks creating systems no one understands. If you're building AI tools for regulated industries or critical applications, ensure explainability doesn’t trade off with performance.
Key Insights for Entrepreneurs
What stood out most to me in the research is the power of understanding and embracing the imperfection at the heart of both adversarial data and human-labeled datasets. This concept connects to broader business lessons for scaling: sometimes, iterating quicker and accepting flawed, incomplete learnings is better than striving for perfect outcomes you can’t afford to create.
In my journey developing startups like CADChain, where AI plays a pivotal role, I’ve seen how leveraging insights from errors can lead to innovations we’d never have predicted using conventional wisdom. Adversarial training and models that “recover” labels from messy data aren’t a crutch, they’re a strategy.
Conclusion
For entrepreneurs peering into the world of artificial intelligence, the lesson from "Adversarial Examples Are Not Bugs, They Are Features" is clear: don’t fear flaws in your data. Instead, learn how your models leverage them. Whether you’re training algorithms for creative industries or protecting intellectual property, understanding how errors distill task-relevant features will make your technology stronger.
Explore adversarial testing tools like Foolbox and dig into the original paper on NeurIPS. Train smarter, not harder, your next breakthrough might just come from the mistakes you decide to embrace.
FAQ
1. What are adversarial examples in machine learning?
Adversarial examples are inputs deliberately modified in ways that are imperceptible to humans but can confuse machine learning models into making incorrect predictions. For example, a slight change in an image of a cat might cause a model to classify it as a dog. Learn more from Distill’s detailed article
2. Why do adversarial examples occur in AI models?
Adversarial examples exploit non-robust patterns in datasets, features that models find predictive but are invisible to humans. These aren't bugs but inherent traits of the model's feature inference. Explore further in “Adversarial Examples Are Not Bugs, They Are Features”
3. Can AI models trained on mislabeled data still perform well?
Yes, research shows that models retrained on adversarially mislabeled datasets can still generalize effectively to clean test data. This underscores the model's ability to abstract useful patterns. Check out Distill’s analysis
4. How does retraining on mislabeled adversarial data benefit AI systems?
Training models on mislabeled adversarial data can extract and reinforce non-obvious patterns, making them robust in noisy, real-world environments. Read more in this detailed publication
5. How does “feature distillation” relate to adversarial examples?
Feature distillation refers to the transfer of feature-related information from one model to another, even when trained on mislabeled or noisy data. This process helps models leverage latent patterns from the original dataset. Learn more about feature distillation concepts
6. What practical advice does this research offer for startups using machine learning?
Startups can use adversarial retraining to enhance model robustness, reduce dependency on perfectly clean datasets, and creatively handle noisy, real-world data for better results. Discover these strategies
7. Are incorrect labels always helpful in training AI systems?
Not all mislabeled datasets are useful; their utility depends on whether the dataset contains relevant structural features for the task. Learn more about the limits of error-data training
8. How can companies identify and utilize adversarial vulnerabilities?
Companies can use tools like Foolbox to create adversarial datasets, identify model vulnerabilities, and retrain their systems for improved robustness. Learn about Foolbox for adversarial testing
9. Does adversarial training sacrifice explainability in AI systems?
Retraining models on adversarial or incomprehensible features can sometimes make those systems harder to interpret, especially for critical industries. Balancing explainability with performance is crucial. Discover challenges in explainability
10. What industries stand to benefit most from adversarial training?
Industries dealing with imperfect data, like creative technologies, intellectual property protection, or real-world messy environments, can greatly benefit from adversarial training frameworks. Explore applications in the NeurIPS paper
About the Author
Violetta Bonenkamp, also known as MeanCEO, is an experienced startup founder with an impressive educational background including an MBA and four other higher education degrees. She has over 20 years of work experience across multiple countries, including 5 years as a solopreneur and serial entrepreneur. Throughout her startup experience she has applied for multiple startup grants at the EU level, in the Netherlands and Malta, and her startups received quite a few of those. She’s been living, studying and working in many countries around the globe and her extensive multicultural experience has influenced her immensely.
Violetta Bonenkamp's expertise in CAD sector, IP protection and blockchain
Violetta Bonenkamp is recognized as a multidisciplinary expert with significant achievements in the CAD sector, intellectual property (IP) protection, and blockchain technology.
CAD Sector:
- Violetta is the CEO and co-founder of CADChain, a deep tech startup focused on developing IP management software specifically for CAD (Computer-Aided Design) data. CADChain addresses the lack of industry standards for CAD data protection and sharing, using innovative technology to secure and manage design data.
- She has led the company since its inception in 2018, overseeing R&D, PR, and business development, and driving the creation of products for platforms such as Autodesk Inventor, Blender, and SolidWorks.
- Her leadership has been instrumental in scaling CADChain from a small team to a significant player in the deeptech space, with a diverse, international team.
IP Protection:
- Violetta has built deep expertise in intellectual property, combining academic training with practical startup experience. She has taken specialized courses in IP from institutions like WIPO and the EU IPO.
- She is known for sharing actionable strategies for startup IP protection, leveraging both legal and technological approaches, and has published guides and content on this topic for the entrepreneurial community.
- Her work at CADChain directly addresses the need for robust IP protection in the engineering and design industries, integrating cybersecurity and compliance measures to safeguard digital assets.
Blockchain:
- Violetta’s entry into the blockchain sector began with the founding of CADChain, which uses blockchain as a core technology for securing and managing CAD data.
- She holds several certifications in blockchain and has participated in major hackathons and policy forums, such as the OECD Global Blockchain Policy Forum.
- Her expertise extends to applying blockchain for IP management, ensuring data integrity, traceability, and secure sharing in the CAD industry.
Violetta is a true multiple specialist who has built expertise in Linguistics, Education, Business Management, Blockchain, Entrepreneurship, Intellectual Property, Game Design, AI, SEO, Digital Marketing, cyber security and zero code automations. Her extensive educational journey includes a Master of Arts in Linguistics and Education, an Advanced Master in Linguistics from Belgium (2006-2007), an MBA from Blekinge Institute of Technology in Sweden (2006-2008), and an Erasmus Mundus joint program European Master of Higher Education from universities in Norway, Finland, and Portugal (2009).
She is the founder of Fe/male Switch, a startup game that encourages women to enter STEM fields, and also leads CADChain, and multiple other projects like the Directory of 1,000 Startup Cities with a proprietary MeanCEO Index that ranks cities for female entrepreneurs. Violetta created the "gamepreneurship" methodology, which forms the scientific basis of her startup game. She also builds a lot of SEO tools for startups. Her achievements include being named one of the top 100 women in Europe by EU Startups in 2022 and being nominated for Impact Person of the year at the Dutch Blockchain Week. She is an author with Sifted and a speaker at different Universities. Recently she published a book on Startup Idea Validation the right way: from zero to first customers and beyond, launched a Directory of 1,500+ websites for startups to list themselves in order to gain traction and build backlinks and is building MELA AI to help local restaurants in Malta get more visibility online.
For the past several years Violetta has been living between the Netherlands and Malta, while also regularly traveling to different destinations around the globe, usually due to her entrepreneurial activities. This has led her to start writing about different locations and amenities from the POV of an entrepreneur. Here’s her recent article about the best hotels in Italy to work from.