Adversarial Examples that Fool both Computer Vision and Time-Limited Humans (1802.08195v3)

Abstract: Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ostrich. However, it is still an open question whether humans are prone to similar mistakes. Here, we address this question by leveraging recent techniques that transfer adversarial examples from computer vision models with known parameters and architecture to other models with unknown parameters and architecture, and by matching the initial processing of the human visual system. We find that adversarial examples that strongly transfer across computer vision models influence the classifications made by time-limited human observers.

• The paper demonstrates that adversarial perturbations can fool both CNNs and time-limited humans, challenging assumptions about human perceptual immunity to minute visual changes.
• It leverages biologically inspired preprocessing and neural network ensembles to mimic early retinal transformations, which facilitates the transfer of adversarial effects.
• Empirical results indicate a marked increase in misclassification rates under time constraints, highlighting shared vulnerabilities in machine learning systems and rapid human decision-making.

Insights from "Adversarial Examples that Fool both Computer Vision and Time-Limited Humans"

The investigation of adversarial examples poses substantial challenges and presents intriguing questions at the intersection of machine learning and human perception. The paper "Adversarial Examples that Fool both Computer Vision and Time-Limited Humans" offers a nuanced exploration of the impact of adversarial examples on both artificial and biological vision systems. Herein, the authors explore adversarial perturbations that transfer not just across machine learning models but also influence human observers under time constraints.

Key Contributions

1. Adversarial Transferability to Humans: The primary contribution of this paper is the demonstration that adversarial examples, traditionally understood within the domain of neural networks, can also deceive time-limited humans. This finding is significant because it challenges the prevalent notion that adversarial perturbations are irrelevant to human perception due to their small magnitude being imperceptible at a conscious level.
2. Impact of Visual Time Constraints: By limiting the time available for image analysis, the paper provides insights into the quick-decision phase of human vision—where adversarial examples could potentially influence classification tasks, mirroring how they affect CNNs.
3. Model Ensembles and Initial Visual Processing: The authors leveraged ensembles of various neural networks enhanced with biologically inspired preprocessing layers, mimicking initial retinal transformations. This approach aimed to align machine vision processing more closely with human physiological processing, facilitating the transfer of adversarial examples.
4. Empirical Results and Measurements: The authors demonstrated strong empirical results, showing significant error rates induced by adversarial examples even within controlled conditions. Notably, adversarially perturbed images led to an increase in incorrect classifications by time-limited human observers, indicating that these perturbations effectively manipulate perceived features of the images.

Implications for Machine Learning and Security

The findings extend beyond theoretical interests; they shed light on potential vulnerabilities in AI systems and imply broader cognitive implications for human viewers. This intersection suggests that features leveraged by adversarial attacks could manifest as cognitive illusions, blurring the lines between machine perception errors and human biases. Such insights could stimulate developments in more robust AI models that incorporate elements observed in higher-level human visual processing, including top-down feedback and lateral interactions.

Moreover, the paper's implications on security are noteworthy. If adversarial examples can influence human perceptions, albeit under specific conditions, this raises concerns over media and imagery manipulation to guide or alter public perceptions. This presents an unexplored avenue of research into psychological and social implications of adversarial techniques beyond AI model integrity.

Future Research Directions

Further research is warranted to explore the boundaries and robustness of adversarial transferability. Key future directions include:

• Exploring Time Constraints: Extending analysis over a broader range of time constraints, to investigate transitions from rapid to more contemplative human classification processes.
• Larger Variability in Perturbation Magnitudes: Analyzing how varying perturbation magnitudes affect transferability.
• Architectural and Methodological Integrations: Develop neural network architectures that mimic advanced human visual processing stages to potentially gain more robust AI models against adversarial perturbations.

In conclusion, this paper contributes a crucial piece to the puzzle of adversarial learning, delineating the surprising reach of adversarial examples into the field of human perception and opening new avenues for interdisciplinary research blending the understanding of machine learning, neuroscience, and cognitive science.