Detector Collapse: A Novel Backdoor Attack Paradigm to Catastrophically Disable Object Detection Systems

核心概念

Detector Collapse (DC) is a novel backdoor attack paradigm designed to instantly incapacitate object detection systems by severely impairing their performance and culminating in a denial-of-service.

要約

The paper introduces Detector Collapse (DC), a groundbreaking backdoor attack paradigm tailored specifically for object detection (OD) tasks. Unlike previous OD backdoor attacks that primarily focused on localized errors, DC aims to indiscriminately degrade the overall performance of OD models.

To achieve this, the authors develop two innovative attack schemes:

1. SPONGE: This strategy triggers widespread misidentifications, flooding the output with a plethora of false positives. This overwhelms the computational resources of the detection system, leading to a significant reduction in processing speed and culminating in a denial-of-service.

2. BLINDING: This approach compromises the model's perception, causing it to classify all objects as the background, thereby rendering them 'invisible' to the OD system.

The paper also introduces a novel poisoning strategy that uses natural semantic features (e.g., a basketball) as triggers, enhancing the robustness of the backdoor in real-world environments. This is in contrast to previous works that relied on fixed-style triggers, which are less adaptable to dynamic real-world conditions.

Extensive evaluations on different detectors across several benchmarks demonstrate the significant improvement (up to 60% absolute and 7x relative) in attack efficacy of DC over state-of-the-art OD backdoor attacks. The authors also show that DC is resistant to potential defenses, such as fine-tuning and pruning.

Finally, the paper presents a physical-world demonstration of DC, showcasing its ability to catastrophically disable object detection systems in real-world settings.

統計

The paper reports the following key metrics:

On MS-COCO dataset, the SPONGE attack can reduce the mAP of Faster R-CNN from 37.4% to 0.4%, and the BLINDING attack can reduce it to 6.6%.
On PASCAL VOC dataset, the SPONGE attack can reduce the mAP of Faster R-CNN from 49.8% to 0.6%, and the BLINDING attack can reduce it to 11.1%.
On average, the SPONGE attack introduces a 7-10x delay in the processing time of a single poisoned image, posing a significant threat to real-time critical detection systems.

引用

"DC is designed to instantly incapacitate detectors (i.e., severely impairing detector's performance and culminating in a denial-of-service)."
"Remarkably, we introduce a novel poisoning strategy exploiting natural objects, enabling DC to act as a practical backdoor in real-world environments."
"Extensive evaluations on different detectors across several benchmarks demonstrate the significant improvement (up to 60% absolute and 7x relative) in attack efficacy of DC over state-of-the-art OD backdoor attacks."

抽出されたキーインサイト

Detector Collapse: Backdooring Object Detection to Catastrophic Overload or Blindness

by Hangtao Zhan... 場所 arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11357.pdf

深掘り質問

How can object detection systems be made more robust against the Detector Collapse attack and similar backdoor threats

To enhance the robustness of object detection systems against the Detector Collapse attack and similar backdoor threats, several strategies can be implemented:

Regular Model Auditing: Implement regular audits and evaluations of the object detection models to detect any anomalies or unexpected behaviors that may indicate the presence of a backdoor.

Diverse Training Data: Utilize diverse and representative training datasets to ensure that the model is exposed to a wide range of scenarios and objects, making it more resilient to targeted backdoor attacks.

Adversarial Training: Incorporate adversarial training techniques during the model training phase to expose the system to potential attack scenarios and improve its ability to withstand backdoor attacks.

Anomaly Detection: Integrate anomaly detection mechanisms into the system to identify unusual patterns or behaviors that may indicate the presence of a backdoor.

Dynamic Trigger Detection: Develop algorithms that can dynamically detect and analyze triggers in input data to prevent the activation of backdoors.

Model Interpretability: Enhance the interpretability of the object detection models to understand their decision-making processes and detect any suspicious patterns that may indicate a backdoor.

What are the potential countermeasures that can be developed to detect and mitigate the impact of such advanced backdoor attacks on object detection models

To detect and mitigate the impact of advanced backdoor attacks like the Detector Collapse attack on object detection models, the following countermeasures can be developed:

Behavioral Analysis: Implement behavioral analysis techniques to monitor the model's performance and detect any deviations from expected behavior that may indicate a backdoor attack.

Input Validation: Validate input data to ensure that it meets certain criteria and does not contain any hidden triggers or malicious patterns that could activate a backdoor.

Model Verification: Verify the integrity of the model during inference to ensure that it has not been compromised or manipulated by a backdoor attack.

Ensemble Learning: Employ ensemble learning techniques to combine multiple models and detect discrepancies or inconsistencies in their predictions, which may signal a backdoor attack.

Post-Deployment Monitoring: Continuously monitor the deployed object detection system for any unusual activities or performance degradation that may be indicative of a backdoor attack.

Response Plans: Develop response plans and protocols to quickly address and mitigate the impact of a detected backdoor attack, including model retraining, data validation, and system updates.

What are the broader implications of the Detector Collapse attack on the reliability and trustworthiness of object detection systems in safety-critical applications, such as autonomous driving

The Detector Collapse attack poses significant challenges to the reliability and trustworthiness of object detection systems in safety-critical applications like autonomous driving.

Safety Concerns: The presence of backdoors in object detection systems can lead to catastrophic failures, compromising the safety of autonomous vehicles and endangering lives on the road.

Security Risks: Backdoor attacks like Detector Collapse can be exploited by malicious actors to manipulate the behavior of object detection systems, leading to potential security breaches and unauthorized access to sensitive information.

Trust Issues: The discovery of backdoors in object detection models can erode trust in the technology and the organizations deploying them, raising concerns about the reliability and integrity of autonomous driving systems.

Regulatory Compliance: The presence of backdoors in safety-critical systems may raise regulatory compliance issues, requiring stringent measures to ensure the security and safety of autonomous vehicles.

Mitigation Strategies: Addressing the risks posed by backdoor attacks requires the development of robust mitigation strategies, continuous monitoring, and proactive measures to safeguard object detection systems in critical applications.