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Protecting computer vision from adversarial attacks

Advances in computer vision and machine learning enable a wide range of technology to perform sophisticated tasks with little or no human supervision. From autonomous drones and self-driving vehicles to medical imaging and product development, many computer applications and robots use visual information to make critical decisions. Cities are increasingly relying on these automated technologies for public safety and infrastructure maintenance.

However, compared to humans, computers see a kind of tunnel vision that leaves them vulnerable to attacks with potential disastrous consequences. For example, a human driver, who sees graffiti covering a stop sign, will still recognize it and stop the car at an intersection. Graffiti can cause a self -driving car, on the other hand, to miss the stop sign and plow the intersection. And, while human minds can filter out all sorts of unusual or extraneous visual information when making a decision, computers will hang small deviations from the expected data.

This is because the brain is infinitely complex and can process a lot of data and past experiences simultaneously to arrive at almost immediate decisions appropriate for the situation. Computers rely on mathematical algorithms trained in datasets. Their creativity and knowledge are constrained by the limitations of technology, mathematics, and human vision.

Malicious actors can take advantage of this vulnerability by changing how a computer views an object, by altering the object itself or some aspect of software involved in vision technology. aw. Other attacks can manipulate computer-made decisions about what it sees. Either way can speak to disaster for individuals, towns, or companies.

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