Observed Signal · Jan 3, 2024 · Research Publication · Source: Trending Topics · Impact: 2/5 · Sentiment: Neutral
Google Research Shows Altered Images Trick Humans and AI Alike
Google researchers found that adversarial attacks — subtle image alterations designed to fool AI models — can also influence human perception. Artificial neural networks (ANNs) and humans are similarly affected when confronted with misleading perturbations. Manipulations such as changing pixel scale or overlaying faint patterns are often invisible to the human eye, yet can cause image recognition systems to misclassify objects, for example identifying a white house and a white van as a sheep. The findings show that computers and humans perceive images differently, but their shared susceptibility stems from general image characteristics rather than specific ANN structures. The research has implications for safety-critical applications such as autonomous driving, where manipulated traffic signs could confuse vehicle AI systems. The researchers emphasize the need for further study to build reliable and safer AI systems.
Google research on adversarial attacks provides insights into AI and human perception with implications for AI safety, but has no direct near-term operational impact on the AdTech/MarTech industry.
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Key Takeaways & Evidence Grounding
- Google researchers found that adversarial image perturbations influence human perception similarly to artificial neural networks (ANNs).
- Adversarial attacks subtly alter images to make AI models misclassify image contents with confidence.
- The research was published in a Google DeepMind blog post.
- Manipulated traffic signs could potentially confuse autonomous vehicles.
- Researchers state that more research is needed to build reliable and safer AI systems.
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