Robustness of Generative Adversarial CLIPs Against Single-Character Adversarial Attacks in Text-to-Image Generation

Robustness of Generative Adversarial CLIPs Against Single-Character Adversarial Attacks in Text-to-Image Generation

Generative Adversarial Networks (GANs) have emerged as a powerful type of generative model, particularly effective at creating images from textual descriptions. Similar to diffusion models, GANs rely on text encoders to extract embeddings from these descriptions. However, this reliance introduces sp...

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Bibliographic Details
Main Authors: Patibandla Chanakya, Putla Harsha, Krishna Pratap Singh
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Single-character attack
GALIP
GAN
CLIP text encoder
Text-to-image generation
Online Access:https://ieeexplore.ieee.org/document/10741578/

Internet

https://ieeexplore.ieee.org/document/10741578/

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