In machine learning, adversarial examples are a fascinating and critical phenomenon. These small, often imperceptible changes to input data can deceive models into making incorrect predictions. Earlier interpretations of such examples cast them as bugs, unwanted outliers in need of resolution. But then Andrew Ilyas and his colleagues proposed a game-changing idea: adversarial examples are not bugs, but features. This fresh perspective has real implications for how researchers and practitioners see robustness in AI systems.
The crux of their argument lies in “non-robust features.” These are patterns in data that models find highly predictive but humans typically overlook because they are subtle or counterintuitive. Think about an image classifier trained on photos of cats and dogs. It might latch onto minute pixel-level inconsistencies unique to one category rather than more human-recognizable elements like fur texture or ear shape. In the Ilyas et al. interpretation, adversarial examples happen when attackers manipulate these non-robust features to mislead a model.
Yet, it’s not enough to stop at this realization. Narrowly defining robustness as resistance to these perturbations means we risk building systems that fail in broader, real-world scenarios. For example, making a model resistant to pixel tweaks might still leave it vulnerable to low-light conditions in a live setting. Researchers such as Justin Gilmer and Dan Hendrycks emphasize this in a response article, calling for an expanded approach to robustness. While adversarial training, a method to make models resistant to attacks, is useful, its limited focus on specific adversarial scenarios (like Lₚ-norm perturbations) doesn’t address the fuller picture.
Why Models Exploit Non-Robust Features
This isn’t some abstract theoretical debate, it’s measurable. Studies (like those cited in Gradient Science) show that models, by nature, exploit statistical correlations in datasets. Some of these correlations may work as shortcuts rather than reflecting the true underlying structure of the data. That’s great for benchmarks and surface-level accuracy, but it leaves the model vulnerable when faced with noise, distortions, or novel variations.
One enlightening experiment involved training a model on “robustified” datasets, filtered to remove non-robust features. These models performed better under real-world distribution shifts but tended to lose some predictive power where those non-robust features previously worked well. It’s a trade-off, as reliance on robust features tends to demand better data and more careful engineering.
What Broader Robustness Really Demands
To move forward, researchers need to expand their view. At its core, robustness should ensure models perform reliably across various conditions, not just adversarial ones. This means considering natural image corruptions, shifts in data distributions, and even scenarios beyond what the model encountered during training.
For example, the team behind the Natural Adversarial Examples benchmark has presented datasets designed to test classifiers in more organic but challenging circumstances. Real-world photo collections, full of edge cases humans find tricky (e.g., unusual viewing angles), revealed hidden vulnerabilities in state-of-the-art computer vision models. Such datasets help assess robustness beyond the controlled lab conditions of traditional benchmarks.
Step-by-Step: Making Robustness More Meaningful
- Redefine Objectives: Instead of purely focusing on adversarial attacks, set goals for performance under varied and dynamic real-world conditions.
- Broaden Perturbation Sets: Include natural corruptions like lighting changes, camera noise, or environmental interference when testing models.
- Combine Training Approaches: Use adversarial training alongside augmentation methods like noise injections and novel data augmentations.
- Shift to Metrics That Matter: Relying solely on standard dataset accuracy metrics misses the point. Include robustness metrics that reflect generalization to unseen or corrupted data.
Missteps That Undermine Progress
A common mistake is over-optimizing for adversarial examples as defined by researchers’ constraints. While making systems robust against pixel modifications or gradient-based attacks is helpful, it doesn’t address broader fragilities. Additionally, relying solely o
FAQ
1. What are adversarial examples in machine learning?
Adversarial examples are small, often imperceptible changes to input data that can deceive machine learning models into making incorrect predictions. Read more about adversarial examples
2. Are adversarial examples considered bugs?
Initially, adversarial examples were seen as bugs, but Andrew Ilyas and his colleagues proposed that they are actually features derived from the data that models exploit for predictions. Discover Ilyas et al.'s groundbreaking idea
3. Why do models exploit non-robust features?
Models exploit statistical correlations in datasets, including non-robust features, as shortcuts for predictions, even if these features are subtle or counterintuitive to humans. Learn more about non-robust features in machine learning
4. What is adversarial training?
Adversarial training is a method to improve the robustness of AI models by exposing them to adversarial examples during training, making them resistant to certain attacks. Explore adversarial training techniques
5. Does adversarial training ensure real-world robustness?
No, adversarial training addresses specific adversarial scenarios but does not fully resolve vulnerabilities under broader real-world conditions such as natural data corruptions or distribution shifts. Understand broader robustness challenges
6. How can researchers enhance robustness in AI systems?
Researchers can redefine robustness goals, broaden the types of perturbations tested, and use diverse training approaches, such as combining adversarial training with natural corruption augmentations. Check strategies to improve robustness
7. Why are natural adversarial examples important?
Natural adversarial examples highlight vulnerabilities in models under real-world scenarios, like unusual viewing angles or environmental distortions, beyond typical adversarial attacks. Explore natural adversarial examples
8. What are the implications of adversarial examples for data collection?
Models relying on robust features demand higher-quality datasets and more careful engineering to ensure reliable predictions across various conditions. Understand the trade-offs with robust models
9. How can robustness metrics improve model evaluation?
Moving beyond standard accuracy to include metrics that assess generalization to unseen or corrupted data ensures models are better prepared for unpredictable scenarios. Read about robustness metrics
10. Can adversarial examples be considered a natural outcome?
Yes, adversarial examples are often a natural consequence of models exploiting superficial data correlations, which may fail under noise or distribution shifts. Discover why adversarial examples are predictable
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.