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Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentation

TL;DR

ObsNet with Local Adversarial Attacks achieves fast and accurate OOD detection in semantic segmentation.

cs.CV 🔴 Advanced 2021-08-04 37 views
Victor Besnier Andrei Bursuc David Picard Alexandre Briot
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semantic segmentation adversarial attacks OOD detection deep learning computer vision

Key Findings

Methodology

This paper introduces a novel architecture called ObsNet, trained with Local Adversarial Attacks (LAA). By decoupling OOD detection from the segmentation task, observing the entire segmentation network instead of just its output, generating training data using blind spots in the segmentation network, and focusing on localized regions in the image to simulate OOD objects, the method is validated across CamVid, StreetHazards, and BDD-Anomaly datasets.

Key Results

  • On the CamVid OOD dataset, ObsNet+LAA achieved a fpr95tpr of 44.6, AuPR of 97.6, and AuRoc of 90.9, significantly outperforming other methods.
  • On the StreetHazards dataset, ObsNet+LAA achieved an AuRoc of 92.7, demonstrating excellent performance.
  • On the BDD Anomaly dataset, ObsNet+LAA outperformed baseline methods in both fpr95tpr and AuRoc metrics.

Significance

This research is significant for both academia and industry, particularly in fields like autonomous driving where reliability is crucial. It addresses the trade-off between accuracy and speed in existing methods, providing a solution that enhances robustness and safety in semantic segmentation tasks.

Technical Contribution

Technical contributions include the introduction of the ObsNet architecture and a training scheme based on Local Adversarial Attacks. Unlike existing methods, ObsNet achieves efficient OOD detection without affecting segmentation accuracy and can be integrated with any pre-trained segmentation network.

Novelty

This is the first approach to use Local Adversarial Attacks for generating training data to improve OOD detection in semantic segmentation. Compared to traditional methods, ObsNet offers a new perspective by observing the entire segmentation network's behavior rather than just its output.

Limitations

  • Requires pre-trained segmentation networks to support adversarial attacks, which may limit the use of certain networks.
  • The observer network has memory and computation overhead equivalent to that of the segmentation network, which may not be ideal for real-time applications.

Future Work

Future research directions include optimizing ObsNet's memory and computational efficiency, exploring more types of adversarial attacks, and validating the method's effectiveness on larger-scale real-world datasets.

AI Executive Summary

In semantic segmentation tasks, existing OOD detection methods often face a trade-off between accuracy and speed, limiting their usability in real-world applications. This paper proposes a new OOD detection architecture, ObsNet, trained with Local Adversarial Attacks (LAA) to address this issue. By decoupling OOD detection from the segmentation task, observing the entire segmentation network instead of just its output, generating training data using blind spots in the segmentation network, and focusing on localized regions in the image to simulate OOD objects, ObsNet achieves fast and accurate OOD detection across multiple datasets.

Experimental results show that ObsNet+LAA performs exceptionally well on the CamVid, StreetHazards, and BDD-Anomaly datasets, particularly achieving a fpr95tpr of 44.6, AuPR of 97.6, and AuRoc of 90.9 on the CamVid OOD dataset, significantly outperforming other methods. This demonstrates that the method achieves efficient OOD detection without affecting segmentation accuracy.

While ObsNet excels in accuracy and speed, its demand for memory and computational resources remains high, which may not be suitable for all real-time applications. Future research can focus on optimizing ObsNet's resource usage and validating its performance on larger-scale real-world datasets.

Deep Analysis

Background

With the widespread adoption of deep neural networks in computer vision, OOD detection in semantic segmentation has become a critical issue. Existing methods often face a trade-off between accuracy and speed, affecting their practicality in real-time applications like autonomous driving. Researchers have attempted to improve OOD detection performance through various methods, such as ensemble methods and auxiliary error prediction modules.

Core Problem

OOD detection in semantic segmentation faces dual challenges of accuracy and speed. Existing methods are either accurate but slow or fast but inaccurate, failing to meet the demands of real-world applications. Achieving efficient OOD detection without compromising segmentation accuracy is a pressing issue.

Innovation

This paper proposes the ObsNet architecture, addressing the shortcomings of existing methods through four design principles: 1) Decoupling OOD detection from the segmentation task; 2) Observing the entire segmentation network instead of just its output; 3) Generating training data using blind spots in the segmentation network; 4) Focusing on localized regions in the image to simulate OOD objects. Compared to traditional methods, this approach offers a new detection perspective.

Methodology

  • �� ObsNet Architecture: Similar to the segmentation network, observing input, output, and intermediate feature maps.
  • �� Local Adversarial Attacks (LAA): Generate adversarial samples in localized regions of the image.
  • �� Training Process: Train ObsNet using samples generated by LAA to predict segmentation network errors.
  • �� Performance Evaluation: Validate ObsNet+LAA performance across multiple datasets.

Experiments

Experiments were conducted on the CamVid, StreetHazards, and BDD-Anomaly datasets, comparing ObsNet+LAA with ten existing methods. Metrics used include fpr95tpr, AuPR, and AuRoc. Various ablation studies were also performed to validate the effectiveness of each design principle.

Results

ObsNet+LAA achieved a fpr95tpr of 44.6, AuPR of 97.6, and AuRoc of 90.9 on the CamVid OOD dataset, significantly outperforming other methods. On the StreetHazards dataset, AuRoc reached 92.7, demonstrating excellent performance. On the BDD Anomaly dataset, ObsNet+LAA outperformed baseline methods in both fpr95tpr and AuRoc metrics.

Applications

The method can be directly applied in fields like autonomous driving, where real-time reliability is crucial. It requires no fine-tuning of pre-trained segmentation networks, making it applicable to various existing architectures.

Limitations & Outlook

While ObsNet excels in accuracy and speed, its demand for memory and computational resources remains high, which may not be suitable for all real-time applications. Additionally, pre-trained segmentation networks must support adversarial attacks, which may limit the use of certain networks. Future research can focus on optimizing ObsNet's resource usage and validating its performance on larger-scale real-world datasets.

Plain Language Accessible to non-experts

Imagine you're in a kitchen, cooking a meal. The segmentation network is like a chef, tasked with identifying ingredients and sorting them into different bowls. However, sometimes the chef encounters unfamiliar ingredients and doesn't know how to handle them. To help the chef recognize these unfamiliar ingredients, we introduce an assistant, ObsNet. This assistant observes all the chef's actions, not just the final dish, and during training, it deliberately gives the chef some strange ingredients (like adversarial attacks) to help the assistant learn to recognize these unfamiliar ingredients. This way, the assistant can alert the chef when encountering unfamiliar ingredients, ensuring the cooking process is safe and accurate.

ELI14 Explained like you're 14

Imagine you're playing a game with many levels, each with different enemies. Your task is to defeat these enemies, but sometimes new monsters appear that you've never seen before, catching you off guard. To help you recognize these new monsters, the game adds an assistant, ObsNet. This assistant watches your entire process of fighting monsters, not just the result of defeating them. During training, the assistant deliberately gives you some strange enemies (like adversarial attacks) to help the assistant learn to recognize these new monsters. So, when you encounter new monsters in the game, the assistant can alert you, helping you prepare for battle!

Glossary

ObsNet

A network architecture for detecting OOD objects in semantic segmentation by observing the entire segmentation network's behavior to predict errors.

ObsNet is used in this paper to achieve fast and accurate OOD detection.

Local Adversarial Attack (LAA)

A method for generating adversarial samples in localized regions of the image, used to train ObsNet for improved OOD detection.

LAA is used to generate training data to help ObsNet recognize OOD objects.

OOD (Out-of-Distribution)

Refers to objects or patterns not present in the training data, and detecting these objects is a major challenge in semantic segmentation.

The goal of this paper is to improve the accuracy of detecting OOD objects.

Semantic Segmentation

A computer vision task that aims to assign a class label to each pixel in an image.

This paper studies methods for detecting OOD objects in semantic segmentation.

fpr95tpr

A metric for evaluating OOD detection performance, representing the false positive rate at 95% true positive rate.

Used to compare the performance of different methods in OOD detection.

Open Questions Unanswered questions from this research

  • 1 How to further improve ObsNet's detection accuracy without increasing computational costs?
  • 2 Are there other types of adversarial attacks that can be used to improve OOD detection?
  • 3 How does ObsNet perform on larger-scale real-world datasets?

Applications

Immediate Applications

Autonomous Driving

ObsNet can be used to enhance the reliability of semantic segmentation in autonomous driving systems, helping to identify unseen obstacles and ensuring driving safety.

Long-term Vision

Intelligent Surveillance

Applying ObsNet in intelligent surveillance systems can improve the accuracy of anomaly detection, enhancing public safety.

Abstract

In this paper, we tackle the detection of out-of-distribution (OOD) objects in semantic segmentation. By analyzing the literature, we found that current methods are either accurate or fast but not both which limits their usability in real world applications. To get the best of both aspects, we propose to mitigate the common shortcomings by following four design principles: decoupling the OOD detection from the segmentation task, observing the entire segmentation network instead of just its output, generating training data for the OOD detector by leveraging blind spots in the segmentation network and focusing the generated data on localized regions in the image to simulate OOD objects. Our main contribution is a new OOD detection architecture called ObsNet associated with a dedicated training scheme based on Local Adversarial Attacks (LAA). We validate the soundness of our approach across numerous ablation studies. We also show it obtains top performances both in speed and accuracy when compared to ten recent methods of the literature on three different datasets.

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