无需重新训练即可操纵与缓解生成模型偏差
Manipulating and Mitigating Generative Model Biases without Retraining
AI总结：
本研究提出无需重训练的T2I生成模型偏差操纵方法，通过向量代数控制语言嵌入调整输出分布，可用于缓解社会偏差也可实现高成功率后门攻击。
摘要：
文本到图像（T2I）生成模型在公众领域的普及度日益提升。尽管这类模型具备出色的用户引导生成能力，但其黑箱特性使用户可能接触到存在人为注入偏差或固有偏差的输出。现有的偏差操纵（及缓解）技术通常依赖对学习参数和训练数据的精细调整，以此修改决策边界来改变模型的偏差特征，这类方法往往计算成本高昂。我们提出一种动态且计算高效的T2I模型偏差操纵方法，无需重新训练模型，仅利用其丰富的语言嵌入空间即可实现。研究表明，借助基础向量代数可便捷控制语言模型嵌入，从而偏移T2I模型的输出、控制生成类别的分布。作为附带效果，这种控制可作为一种精准的提示工程手段，生成常规文本提示通常难以生成的不合理图像。我们通过平衡生成图像中社会群体的出现频率验证了该技术的建设性应用，在三个社会偏差维度上有效均衡了类别分布。同时，我们也揭示了偏差操纵的负面影响：将该方法构建为使用语义空输入触发器、可控制严重程度的后门攻击，实验显示其攻击成功率最高可达100%。
英文摘要：
Text-to-image (T2I) generative models have gained increased popularity in the public domain. While boasting impressive user-guided generative abilities, their black-box nature exposes users to intentionally- and intrinsically-biased outputs. Bias manipulation (and mitigation) techniques typically rely on careful tuning of learning parameters and training data to adjust decision boundaries to influence model bias characteristics, which is often computationally demanding. We propose a dynamic and computationally efficient manipulation of T2I model biases by exploiting their rich language embedding spaces without model retraining. We show that leveraging foundational vector algebra allows for a convenient control over language model embeddings to shift T2I model outputs and control the distribution of generated classes. As a by-product, this control serves as a form of precise prompt engineering to generate images which are generally implausible using regular text prompts. We demonstrate a constructive application of our technique by balancing the frequency of social classes in generated images, effectively balancing class distributions across three social bias dimensions. We also highlight a negative implication of bias manipulation by framing our method as a backdoor attack with severity control using semantically-null input triggers, reporting up to 100% attack success rate. Key-words: Text-to-Image Models, Generative Models, Bias, Prompt Engineering, Backdoor Attacks
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
