Few-Shot Adversarial Prompt Learning on Vision-Language Models: Addressing Limitations and Proposing a Novel Framework

Konsep Inti

Proposing a few-shot adversarial prompt framework to enhance robustness in vision-language models by addressing key limitations.

Abstrak

The content discusses the vulnerability of deep neural networks to imperceptible adversarial perturbations and introduces a few-shot adversarial prompt framework to improve robustness. It addresses issues with previous methods, such as heavy adaptation costs and suboptimal text supervision. The proposed framework leverages adversarially correlated text supervision and a novel training objective to enhance consistency of multi-modal features.

Directory:

1. Abstract
   • Discusses the vulnerability of deep neural networks to imperceptible adversarial perturbations.
   • Introduces a few-shot adversarial prompt framework to improve robustness.
2. Introduction
   • Highlights the challenges posed by adversarial examples in misleading DNNs.
   • Discusses the importance of semantic information for human cognition compared to statistical associations in machines.
3. Method
   • Introduces the Few-shot Adversarial Prompt learning (FAP) framework for adapting pre-trained VLMs in a few-shot manner.
   • Describes learnable text supervision for adversarial examples and balancing natural and adversarial generalization.
4. Experiments
   • Evaluates the performance of the proposed method on various datasets, showcasing superior results in both natural and robust accuracy.
5. Conclusion
   • Summarizes the contributions of the research in enhancing model robustness against adversarial attacks.

Statistik

"achieves zero-shot adversarial robustness by aligning adversarial visual features with text supervision."
"matches state-of-the-art zero-shot adversarial robustness with only 1% training data."

Kutipan

"The proposed framework gives access to learn adversarial text supervision, which provides superior cross-modal adversarial alignment."
"Our method matches the benchmark result with 1.25% examples from ImageNet, thus speeding up the training process."

Wawasan Utama Disaring Dari

Few-Shot Adversarial Prompt Learning on Vision-Language Models

by Yiwei Zhou,X... pada arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.14774.pdf

Pertanyaan yang Lebih Dalam

How can the proposed FAP framework be applied to other domains beyond vision-language models

The proposed Few-Shot Adversarial Prompt (FAP) framework can be applied to various domains beyond vision-language models by adapting the core principles of adversarial prompt learning. One potential application is in natural language processing tasks, where models can benefit from robustness against adversarial attacks. By incorporating learnable prompts and a novel training objective that balances natural and adversarial generalization, NLP models can enhance their resilience to malicious perturbations. Additionally, FAP could be extended to reinforcement learning settings, enabling agents to learn robust policies with limited data through adversarially correlated supervision. This approach could improve the security and reliability of AI systems in dynamic environments.

What are potential drawbacks or criticisms of relying on few-shot learning for improving model robustness

While few-shot learning offers advantages in improving model robustness with limited data, there are potential drawbacks and criticisms associated with this approach:

Limited Generalization: Few-shot learning may not capture the full complexity of real-world scenarios due to its reliance on a small number of examples. Models trained using few shots may struggle to generalize well outside the specific context they were trained on.

Overfitting: With only a few examples per class or task, there is a risk of overfitting during training, especially when adapting complex models like vision-language frameworks. Overfitting can lead to reduced performance on unseen data or new tasks.

Sensitivity to Noise: Few-shot learning methods are more susceptible to noise or outliers in the training data since they have fewer samples for regularization or error correction.

Scalability Issues: Scaling up few-shot approaches to larger datasets or more complex tasks may pose challenges in terms of computational resources and training time.

Lack of Diversity: Limited diversity in the few-shot dataset may result in biased representations and hinder model performance across diverse inputs.

How might advancements in this area impact real-time applications like mobile device security

Advancements in improving model robustness through techniques like FAP could have significant implications for real-time applications such as mobile device security:

Enhanced Security Measures: By integrating FAP into mobile AI systems, devices can better defend against adversarial attacks aimed at compromising sensitive information or manipulating system behavior.

Improved Privacy Protection: Robust models developed using FAP can strengthen privacy measures by detecting and mitigating privacy breaches caused by malicious inputs.

3Real-Time Threat Detection: The ability of FAP-enhanced models to quickly adapt and identify threats makes them valuable for real-time threat detection applications on mobile devices.
4Resilience Against Attacks: Mobile device security protocols leveraging FAP-based defenses will exhibit greater resilience against evolving attack strategies targeting vulnerabilities within AI systems deployed on these devices.