Artificial intelligence (AI) is more and more based mostly on machine learning models, skilled utilizing giant datasets. Likewise, human-computer interplay is more and more depending on speech communication, primarily as a result of exceptional efficiency of machine learning models in speech recognition duties.
However, these models will be fooled by “adversarial” examples; in different phrases, inputs deliberately perturbed to provide a improper prediction with out the modifications being observed by people. “Suppose we have a model that classifies audio (e.g., voice command recognition) and we want to deceive it; in other words, generate a perturbation that maliciously prevents the model from working properly. If a signal is heard properly, a person is able to notice whether a signal says ‘yes,’ for example. When we add an adversarial perturbation we will still hear ‘yes,’ but the model will start to hear ‘no,’ or ‘turn right’ instead of left or any other command we don’t want to execute,” defined Jon Vadillo, researcher within the UPV/EHU’s Departament of Computer Science and Artificial Intelligence.
This may have “very serious implications at the level of applying these technologies to real-world or highly sensitive problems,” added Vadillo. It stays unclear why this occurs. Why would a mannequin that behaves so intelligently all of a sudden cease working correctly when it receives even barely altered alerts?
Deceiving the mannequin by utilizing an undetectable perturbation
“It is important to know whether a model or a program has vulnerabilities,” added the researcher from the Faculty of Informatics. “Firstly, we investigate these vulnerabilities, to check that they exist, and because that is the first step in eventually fixing them.” While a lot analysis has targeted on the event of new strategies for producing adversarial perturbations, much less consideration has been paid to the points that decide whether or not these perturbations will be perceived by people and what these points are like. This concern is vital, because the adversarial perturbation methods proposed solely pose a menace if the perturbations can’t be detected by people.
This research has investigated the extent to which the distortion metrics proposed within the literature for audio adversarial examples can reliably measure the human notion of perturbations. In an experiment during which 36 folks evaluated adversarial examples or audio perturbations in response to numerous components, the researchers confirmed that “the metrics that are being used by convention in the literature are not completely robust or reliable. In other words, they do not adequately represent the auditory perception of humans; they may tell you that a perturbation cannot be detected, but then when we evaluate it with humans, it turns out to be detectable. So we want to issue a warning that due to the lack of reliability of these metrics, the study of these audio attacks is not being conducted very well,” stated the researcher.
In addition, the researchers have proposed a extra strong analysis technique that’s the consequence of the “analysis of certain properties or factors in the audio that are relevant when assessing detectability, for example, the parts of the audio in which a perturbation is most detectable.” Even so, “this problem remains open because it is very difficult to come up with a mathematical metric that is capable of modeling auditory perception. Depending on the type of audio signal, different metrics will probably be required or different factors will need to be considered. Achieving general audio metrics that are representative is a complex task,” concluded Vadillo.
Jon Vadillo et al, On the human analysis of common audio adversarial perturbations, Computers & Security (2021). DOI: 10.1016/j.cose.2021.102495
University of the Basque Country
Seeking a way of preventing audio models for AI machine learning from being fooled (2022, January 6)
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