Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects (1811.11553v3)

Abstract: Despite excellent performance on stationary test sets, deep neural networks (DNNs) can fail to generalize to out-of-distribution (OoD) inputs, including natural, non-adversarial ones, which are common in real-world settings. In this paper, we present a framework for discovering DNN failures that harnesses 3D renderers and 3D models. That is, we estimate the parameters of a 3D renderer that cause a target DNN to misbehave in response to the rendered image. Using our framework and a self-assembled dataset of 3D objects, we investigate the vulnerability of DNNs to OoD poses of well-known objects in ImageNet. For objects that are readily recognized by DNNs in their canonical poses, DNNs incorrectly classify 97% of their pose space. In addition, DNNs are highly sensitive to slight pose perturbations. Importantly, adversarial poses transfer across models and datasets. We find that 99.9% and 99.4% of the poses misclassified by Inception-v3 also transfer to the AlexNet and ResNet-50 image classifiers trained on the same ImageNet dataset, respectively, and 75.5% transfer to the YOLOv3 object detector trained on MS COCO.

• The paper finds Deep Neural Networks are highly vulnerable to misclassifying familiar objects shown in unusual poses, failing in 97% of out-of-distribution configurations.
• Adversarial poses generated from 3D models transfer effectively between different network architectures, with transferability rates exceeding 99.4%.
• Current adversarial training and data augmentation methods are insufficient to address this vulnerability, suggesting a need for models with richer, potentially 3D-aware, representations.

An Analysis of Deep Neural Network Vulnerability to Out-of-Distribution Poses

The paper "Strike (with) a Pose: Neural Networks Are Easily Fooled" investigates the susceptibility of Deep Neural Networks (DNNs) to misclassifications when presented with out-of-distribution (OoD) poses of widely recognized objects. By leveraging 3D rendering technology, the authors synthesize poses that, although recognizably human, cause DNNs to deviate from expected performance. This paper highlights the gaps between the robust image classification abilities of DNNs in controlled environments and their inability to generalize across natural, unanticipated views of objects.

Overview

The research tackles a critical issue in the application of DNNs: the overconfidence of these models when dealing with OoD scenarios. This is particularly relevant in contexts such as autonomous vehicles and robotics, where the real-world data distribution differs substantially from the training-set data. The authors developed a framework to explore and quantify these vulnerabilities, utilizing 3D models and rendering techniques to methodically expose DNNs to altered object poses.

Methodological Insights

The core methodology revolves around rendering 3D object models from varying angles and distances, transforming these into 2D images, and subsequently analyzing DNN responses. The paper employs a systematic approach to locate OoD poses by optimizing pose parameters so that they lead to DNN misclassifications. This is achieved through a blend of gradient-based methods and random search within the six-dimensional parameter space defining 3D translations and rotations.

Notably, the authors demonstrate that DNNs fail to recognize familiar objects in 97% of their possible pose configurations outside the trained distribution. They employ both non-differentiable and differentiable renderers to fine-tune this methodology, although the former yielded more stable gradient approximations.

Key Findings

A startling conclusion from this work is the ubiquity of adversarial poses across multiple DNN architectures. The paper found that adversarial poses misclassified by one network often led to misclassifications in others, with transferability rates exceeding 99.4% between different networks on the same dataset.

Furthermore, training on adversarial examples derived from 3D models did not substantially improve generalization on unseen objects, suggesting current modes of adversarial training and dataset augmentation are insufficient to close the gap.

Implications and Future Directions

This paper points to significant practical implications for AI deployment in dynamic, real-world settings. The sensitivity of neural networks to trivial pose modifications underscores the necessity for models that incorporate richer, more invariant representations, possibly through enhanced use of 3D data and mechanisms for visual reasoning.

Theoretically, the paper calls for deeper exploration into the nature of adversarial examples, particularly around the geometric features contributing to DNN fragility. This might involve extending the framework to encompass dynamic scenes, multiview renderings, and leveraging GANs for adversarial example generation.

The findings suggest fertile ground for future research to develop models that inherently perceive and reason in three dimensions, aligning closer with human cognitive processes. Indeed, the integration of more complex data augmentation techniques or redesigning model architectures to internalize 3D transformations could yield more robust AI systems against adversarial attacks.

In conclusion, while DNNs have demonstrated remarkable proficiency in structured environments, this research uncovers latent vulnerabilities when confronted with the full complexity of real-world scenarios, providing a pathway to enhance our understanding and fortification of AI models against such adversarial threats.