What is the McGurk effect?
When the perception of what we hear is influenced by what we see.
Recent work on adversarial examples has demonstrated that most natural inputs can be perturbed to fool even state-of-the-art machine learning systems. But does this happen for humans as well? In this work, we investigate: what fraction of natural instances of speech can be turned into"illusions"which either alter humans' perception or result in different people having significantly different perceptions? We first consider the McGurk effect, the phenomenon by which adding a carefully chosen video clip to the audio channel affects the viewer's perception of what is said (McGurk and MacDonald, 1976). We obtain empirical estimates that a significant fraction of both words and sentences occurring in natural speech have some susceptibility to this effect. We also learn models for predicting McGurk illusionability. Finally we demonstrate that the Yanny or Laurel auditory illusion (Pressnitzer et al., 2018) is not an isolated occurrence by generating several very different new instances. We believe that the surprising density of illusionable natural speech warrants further investigation, from the perspectives of both security and cognitive science. Supplementary videos are available at: https://www.youtube.com/playlist?list=PLaX7t1K-e_fF2iaenoKznCatm0RC37B_k.
A growing body of work on adversarial examples has identified that for machine-learning (ML) systems that operate on high-dimensional data, for nearly every natural input there exists a small perturbation of the point that will be misclassified by the system, posing a threat to its deployment in certain critical settings BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 . More broadly, the susceptibility of ML systems to adversarial examples has prompted a re-examination of whether current ML systems are truly learning or if they are assemblages of tricks that are effective yet brittle and easily fooled BIBREF9 . Implicit in this line of reasoning is the assumption that instances of ”real" learning, such as human cognition, yield extremely robust systems. Indeed, at least in computer vision, human perception is regarded as the gold-standard for robustness to adversarial examples. Evidently, humans can be fooled by a variety of illusions, whether they be optical, auditory, or other; and there is a long line of research from the cognitive science and psychology communities investigating these BIBREF10 . In general, however, these illusions are viewed as isolated examples that do not arise frequently, and which are far from the instances encountered in everyday life. In this work, we attempt to understand how susceptible humans' perceptual systems for natural speech are to carefully designed “adversarial attacks.” We investigate the density of certain classes of illusion, that is, the fraction of natural language utterances whose comprehension can be affected by the illusion. Our study centers around the McGurk effect, which is the well-studied phenomenon by which the perception of what we hear can be influenced by what we see BIBREF0 . A prototypical example is that the audio of the phoneme “baa,” accompanied by a video of someone mouthing “vaa”, can be perceived as “vaa” or “gaa” (Figure 1 ). This effect persists even when the subject is aware of the setup, though the strength of the effect varies significantly across people and languages and with factors such as age, gender, and disorders BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 , BIBREF19 . A significant density of illusionable instances for humans might present similar types of security risks as adversarial examples do for ML systems. Auditory signals such as public service announcements, instructions sent to first responders, etc., could be targeted by a malicious agent. Given only access to a screen within eyesight of the intended victims, the agent might be able to significantly obfuscate or alter the message perceived by those who see the screen (even peripherally).