3 ms·

Is this not a whole new attack surface?

• TheArcane 8y ago
  Not really. In addition to object classes, object detection networks learn to ID teh context of an object's presence. In almost every scenario, this context is a desirable learning trait.
  • sorokod 8y ago
    How about a scenario where an image of an elephant ( or any other out of context example ) is projected on purpose with intent to cause damage?
• QML 8y ago
  Adversarially generated inputs have been known for some time [1], so I wouldn’t call it a new class of attacks unless 1. there are other and older ways of attacking neural networks 2. new as in the last couple of years. [1] https://blog.openai.com/adversarial-example-research/ https://blog.openai.com/adversarial-example-research/
  • p1esk 8y ago
    Yes, this can be used for generating adversarial inputs, and yes, this is a new attack surface. From the paper: The images generated here could be viewed as a variant of adversarial examples, in which small image perturbations (imperceptible to humans) cause a large shift in the network’s output. The images we generate are of a somewhat opposite flavor: while we do not limit the magnitude of the difference between the original and modified image, the detectors are sometimes “blind” to the inserted object. In addition, our examples are not “targeted” in the sense that no optimization process is required to generate them; they seem prevalent enough so that a simple scan of transplanting translated versions of one object in the other can give rise to multiple wrong interpretations.