Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid (1708.06939v1)

Abstract: Deep neural networks have been widely adopted in recent years, exhibiting impressive performances in several application domains. It has however been shown that they can be fooled by adversarial examples, i.e., images altered by a barely-perceivable adversarial noise, carefully crafted to mislead classification. In this work, we aim to evaluate the extent to which robot-vision systems embodying deep-learning algorithms are vulnerable to adversarial examples, and propose a computationally efficient countermeasure to mitigate this threat, based on rejecting classification of anomalous inputs. We then provide a clearer understanding of the safety properties of deep networks through an intuitive empirical analysis, showing that the mapping learned by such networks essentially violates the smoothness assumption of learning algorithms. We finally discuss the main limitations of this work, including the creation of real-world adversarial examples, and sketch promising research directions.

• The paper demonstrates that slight adversarial perturbations can mislead the iCub’s deep learning-based vision system, exposing safety risks in autonomous applications.
• The study employs a rigorous methodology to generate adversarial examples, revealing significant vulnerabilities even under high confidence misclassification scenarios.
• The paper proposes an efficient countermeasure using a reject option strategy, balancing the trade-off between rejecting adversarial inputs and avoiding false negatives.

Vulnerability of Robot Vision Systems to Adversarial Perturbations: An Examination

The paper "Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid" presents a critical analysis of the susceptibility of deep learning algorithms employed in robot-vision systems to adversarial perturbations. The authors evaluate the extent of vulnerability of such systems, focusing on the iCub humanoid, to adversarial examples, which consist of images containing subtle noise modifications intended to mislead classification. Moreover, they propose a computationally efficient countermeasure to mitigate these adversarial threats by rejecting the classification of anomalous inputs.

Overview of the Research

Deep learning algorithms are becoming increasingly integral in various applications, including computer vision, speech recognition, and human-robot interaction. Despite their success, these algorithms face significant challenges, particularly their vulnerability to adversarial examples. The paper explores the vulnerability of robot-vision systems, whose design relies on deep-learning techniques, to adversarial interference. This paper focuses on the iCub humanoid robot, which utilizes deep learning for object recognition and interaction with its environment.

Methodological Approach and Results

The authors employ a robust methodological framework to generate adversarial examples, moving beyond the convention of employing minimal perturbations. Their approach assesses the security of the iCub system, taking into consideration the probabilities of evading detection with higher confidence levels under maximum input perturbations. The paper highlights the deficits in the smoothness assumption of learning algorithms, demonstrating how small perturbations can cause disproportionately large changes in the deep feature space learned by these neural networks.

Empirical evaluations reveal that the iCub's vision system is susceptible to adversarial examples at varying levels of perturbation. The proposed countermeasure based on classifying with the reject option demonstrates substantial improvements in mitigating these adversarial effects. Through adjusting discrimination thresholds, the system enhances its capability to reject suspicious inputs, although a trade-off exists between rejection rate and the likelihood of incorrectly rejecting legitimate samples.

Implications and Future Directions

The implications of this research are profound in safety-critical applications, highlighting the need for enhanced security features in AI systems deployed in contexts involving physical interaction with humans. The vulnerability of deep learning systems to adversarial examples may lead to severe consequences in autonomous systems, where incorrect object recognition can lead to dangerous behaviors. The paper proposes a foundation for improving deep feature space stability by enforcing constraints during network training—an avenue for future exploration.

Further research could focus on developing more robust defenses against adversarial examples, possibly through retraining deep networks to offer greater resistance to adversarial noise. Moreover, exploring security threats such as poisoning attacks, where a malicious entity might provide false data to corrupt the learning model, could broaden the understanding of vulnerabilities affecting robot systems.

Conclusion

The exploration of adversarial threats to the iCub humanoid offers valuable insights into the broader challenges facing AI in autonomous systems. With a call for more robust security frameworks and defense mechanisms, this paper represents a significant contribution toward ensuring the safe deployment of AI technologies in areas with direct human impact. Continued research and partnerships across fields are critical for advancing security in AI, with significant implications for both theoretical development and practical applications.