insight - Machine Learning - # Energy-Latency Manipulation in VLMs

Inducing High Energy-Latency of Large Vision-Language Models with Verbose Images

Q: How can the use of verbose images impact real-world applications of VLMs?

The use of verbose images can have significant implications for real-world applications of Vision-Language Models (VLMs). By crafting imperceptible perturbations in images to induce VLMs to generate longer sequences, the energy-latency cost during inference can be increased. This could lead to a more resource-intensive deployment process for VLMs, potentially affecting their scalability and efficiency in practical applications. In scenarios where computational resources are limited or where quick responses are crucial, the induced high energy-latency costs from verbose images could hinder the performance and responsiveness of VLM-based systems. Additionally, if attackers exploit this vulnerability by deploying verbose images maliciously, it could disrupt services relying on VLM technology.

Q: How might the findings of this study influence future research on security vulnerabilities in machine learning models?

The findings from this study shed light on a novel attack surface concerning availability issues with large Vision-Language Models (VLMs) induced by high energy consumption and latency costs during inference. This research underscores the importance of considering not only model accuracy but also potential security vulnerabilities related to resource consumption. Future research in security vulnerabilities in machine learning models may benefit from exploring similar attack vectors that target different types of models beyond VLMs. Understanding how adversarial inputs like verbose images can impact various machine learning architectures will be essential for developing robust defenses against such attacks. Moreover, researchers may delve deeper into mitigating strategies and countermeasures to protect against these types of attacks. Developing techniques that enhance model resilience without compromising performance will be crucial as machine learning technologies continue to advance and become more integrated into critical systems.

Q: What potential ethical concerns arise from inducing high energy-latency costs in VLMs?

Inducing high energy-latency costs in Vision-Language Models (VLMs) through techniques like crafting verbose images raises several ethical concerns: Resource Consumption: Excessive energy usage due to prolonged latency times can have environmental implications, contributing to increased carbon footprints and electricity consumption. Service Disruption: Deliberately inducing high energy-latency costs could disrupt services relying on efficient inference speeds provided by VMLs, impacting user experience and service reliability. Fairness: If certain users or communities disproportionately bear the brunt of slower response times caused by resource-intensive attacks on VML systems, it could exacerbate existing inequalities. Security Risks: Exploiting vulnerabilities related to excessive resource consumption may open doors for further cyber threats targeting sensitive data processed by vulnerable models. Transparency & Accountability: Ensuring transparency about potential risks associated with such attacks is crucial for maintaining accountability among stakeholders involved in deploying AI technologies. Addressing these ethical concerns requires a balanced approach that considers both technological advancements and societal impacts while promoting responsible AI development practices within an ethical framework.

Core Concepts

Verbose images can induce high energy-latency cost in VLMs by increasing the length of generated sequences.

Abstract

The content discusses inducing high energy-latency costs in large vision-language models (VLMs) using verbose images. It explores the relationship between energy consumption, latency time, and sequence length in VLMs during inference. The proposed method involves delaying the end-of-sequence token, enhancing output uncertainty, improving token diversity, and utilizing a temporal weight adjustment algorithm to craft imperceptible perturbations. Extensive experiments show that verbose images significantly increase the length of generated sequences compared to original images on MS-COCO and ImageNet datasets.
Abstract:

Large vision-language models (VLMs) require substantial computational resources for deployment.
Attackers can induce high energy consumption and latency time during inference to exhaust resources.
Verbose images are proposed to manipulate VLMs into generating longer sequences.
Loss objectives delay EOS occurrence, enhance output uncertainty, improve token diversity.
A temporal weight adjustment algorithm balances these losses for effective optimization.
Introduction:

VLMs have achieved remarkable performance but require significant computational resources.
Malicious attacks inducing high energy-latency cost can deplete computational resources.
Proposed verbose images aim to increase sequence length by manipulating VLM outputs.
Methodology:

Three loss objectives are designed: delayed EOS occurrence, enhanced output uncertainty, improved token diversity.
A temporal weight adjustment algorithm is introduced to balance these losses during optimization.
GradCAM visualization shows dispersed attention in verbose images compared to original images.
Results:

Extensive experiments demonstrate that verbose images significantly increase the length of generated sequences in VLMs.
Visual interpretation using GradCAM highlights dispersed attention in verbose images.
Textual interpretation shows increased object hallucination in sequences generated from verbose images.

Stats

"Extensive experiments demonstrate that our verbose images can increase the length of generated sequences by 7.87× and 8.56× compared to original images on MS-COCO and ImageNet datasets."

Quotes

"Our contribution can be outlined as follows."
"Our code is available at https://github.com/KuofengGao/Verbose_Images."

Key Insights Distilled From

Inducing High Energy-Latency of Large Vision-Language Models with Verbose Images

by Kuofeng Gao,... at arxiv.org 03-25-2024

https://arxiv.org/pdf/2401.11170.pdf

Inducing High Energy-Latency of Large Vision-Language Models with Verbose Images

Deeper Inquiries

How can the use of verbose images impact real-world applications of VLMs?

The use of verbose images can have significant implications for real-world applications of Vision-Language Models (VLMs). By crafting imperceptible perturbations in images to induce VLMs to generate longer sequences, the energy-latency cost during inference can be increased. This could lead to a more resource-intensive deployment process for VLMs, potentially affecting their scalability and efficiency in practical applications.
In scenarios where computational resources are limited or where quick responses are crucial, the induced high energy-latency costs from verbose images could hinder the performance and responsiveness of VLM-based systems. Additionally, if attackers exploit this vulnerability by deploying verbose images maliciously, it could disrupt services relying on VLM technology.

How might the findings of this study influence future research on security vulnerabilities in machine learning models?

The findings from this study shed light on a novel attack surface concerning availability issues with large Vision-Language Models (VLMs) induced by high energy consumption and latency costs during inference. This research underscores the importance of considering not only model accuracy but also potential security vulnerabilities related to resource consumption.
Future research in security vulnerabilities in machine learning models may benefit from exploring similar attack vectors that target different types of models beyond VLMs. Understanding how adversarial inputs like verbose images can impact various machine learning architectures will be essential for developing robust defenses against such attacks.
Moreover, researchers may delve deeper into mitigating strategies and countermeasures to protect against these types of attacks. Developing techniques that enhance model resilience without compromising performance will be crucial as machine learning technologies continue to advance and become more integrated into critical systems.

What potential ethical concerns arise from inducing high energy-latency costs in VLMs?

Inducing high energy-latency costs in Vision-Language Models (VLMs) through techniques like crafting verbose images raises several ethical concerns:

Resource Consumption: Excessive energy usage due to prolonged latency times can have environmental implications, contributing to increased carbon footprints and electricity consumption.

Service Disruption: Deliberately inducing high energy-latency costs could disrupt services relying on efficient inference speeds provided by VMLs, impacting user experience and service reliability.

Fairness: If certain users or communities disproportionately bear the brunt of slower response times caused by resource-intensive attacks on VML systems, it could exacerbate existing inequalities.

Security Risks: Exploiting vulnerabilities related to excessive resource consumption may open doors for further cyber threats targeting sensitive data processed by vulnerable models.

Transparency & Accountability: Ensuring transparency about potential risks associated with such attacks is crucial for maintaining accountability among stakeholders involved in deploying AI technologies.

Addressing these ethical concerns requires a balanced approach that considers both technological advancements and societal impacts while promoting responsible AI development practices within an ethical framework.

Inducing High Energy-Latency of Large Vision-Language Models with Verbose Images