Research over the past few years has shown that machine learning (ML) models are vulnerable to adversarial inputs, where an adversary can craft inputs to strategically alter the model’s output (in image classification, speech recognition, or fraud detection). For example, imagine you have deployed a model that identifies your employees based on images of their faces. As demonstrated in the whitepaper Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition, malicious employees may apply subtle but carefully designed modifications to their image and fool the model to authenticate them as other employees. Obviously, such adversarial inputs—especially if there are a significant amount of them—can have a devastating business impact.
Ideally, we want to detect each time an adversarial input is sent to the model to quantify how adversarial inputs are impacting your model and business. To this end, a wide class of methods analyze individual model inputs

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