On the Fairness, Diversity and Reliability of Text-to-Image Generative Models

• URL: http://arxiv.org/abs/2411.13981v1
• Date: Thu, 21 Nov 2024 09:46:55 GMT
• Title: On the Fairness, Diversity and Reliability of Text-to-Image Generative Models
• Authors: Jordan Vice, Naveed Akhtar, Richard Hartley, Ajmal Mian,
• Abstract summary: multimodal generative models have sparked critical discussions on their fairness, reliability, and potential for misuse. We propose an evaluation framework designed to assess model reliability through their responses to perturbations in the embedding space. Our method lays the groundwork for detecting unreliable, bias-injected models and retrieval of bias provenance.
• Score: 49.60774626839712
• License:
• Abstract: The widespread availability of multimodal generative models has sparked critical discussions on their fairness, reliability, and potential for misuse. While text-to-image models can produce high-fidelity, user-guided images, they also exhibit unpredictable behavior and vulnerabilities, which can be exploited to manipulate class or concept representations. To address this, we propose an evaluation framework designed to assess model reliability through their responses to globally- and locally-applied `semantic' perturbations in the embedding space, pinpointing inputs that trigger unreliable behavior. Our approach offers deeper insights into two essential aspects: (i) generative diversity, evaluating the breadth of visual representations for learned concepts, and (ii) generative fairness, examining how removing concepts from input prompts affects semantic guidance. Beyond these evaluations, our method lays the groundwork for detecting unreliable, bias-injected models and retrieval of bias provenance. We will release our code. Keywords: Fairness, Reliability, AI Ethics, Bias, Text-to-Image Models

Related papers

• Text-to-Image Representativity Fairness Evaluation Framework [0.42970700836450487]
  We propose Text-to-Image (TTI) Representativity Fairness Evaluation Framework. In this framework, we evaluate three aspects of a TTI system; diversity, inclusion, and quality. The evaluation of our framework on Stable Diffusion shows that the framework can effectively capture the bias in TTI systems.
  arXiv  Detail & Related papers  (2024-10-18T06:31:57Z)
• Unsupervised Model Diagnosis [49.36194740479798]
  This paper proposes Unsupervised Model Diagnosis (UMO) to produce semantic counterfactual explanations without any user guidance. Our approach identifies and visualizes changes in semantics, and then matches these changes to attributes from wide-ranging text sources.
  arXiv  Detail & Related papers  (2024-10-08T17:59:03Z)
• Six-CD: Benchmarking Concept Removals for Benign Text-to-image Diffusion Models [58.74606272936636]
  Text-to-image (T2I) diffusion models have shown exceptional capabilities in generating images that closely correspond to textual prompts. The models could be exploited for malicious purposes, such as generating images with violence or nudity, or creating unauthorized portraits of public figures in inappropriate contexts. concept removal methods have been proposed to modify diffusion models to prevent the generation of malicious and unwanted concepts.
  arXiv  Detail & Related papers  (2024-06-21T03:58:44Z)
• I Bet You Did Not Mean That: Testing Semantic Importance via Betting [8.909843275476264]
  We formalize the global (i.e., over a population) and local (i.e. for a sample) statistical importance of semantic concepts for the predictions of opaque models by means of conditional independence. We use recent ideas of sequential kernelized independence testing to induce a rank of importance across concepts, and showcase the effectiveness and flexibility of our framework.
  arXiv  Detail & Related papers  (2024-05-29T14:51:41Z)
• Evaluating Text-to-Image Generative Models: An Empirical Study on Human Image Synthesis [21.619269792415903]
  We present an empirical study introducing a nuanced evaluation framework for text-to-image (T2I) generative models. Our framework categorizes evaluations into two distinct groups: first, focusing on image qualities such as aesthetics and realism, and second, examining text conditions through concept coverage and fairness.
  arXiv  Detail & Related papers  (2024-03-08T07:41:47Z)
• Bridging Generative and Discriminative Models for Unified Visual Perception with Diffusion Priors [56.82596340418697]
  We propose a simple yet effective framework comprising a pre-trained Stable Diffusion (SD) model containing rich generative priors, a unified head (U-head) capable of integrating hierarchical representations, and an adapted expert providing discriminative priors. Comprehensive investigations unveil potential characteristics of Vermouth, such as varying granularity of perception concealed in latent variables at distinct time steps and various U-net stages. The promising results demonstrate the potential of diffusion models as formidable learners, establishing their significance in furnishing informative and robust visual representations.
  arXiv  Detail & Related papers  (2024-01-29T10:36:57Z)
• Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization [73.89239820192894]
  We argue that automated counterfactual generation should regard several aspects of the produced adversarial instances. We present a novel framework for the generation of counterfactual examples.
  arXiv  Detail & Related papers  (2022-05-20T15:02:53Z)
• Trusted Multi-View Classification with Dynamic Evidential Fusion [73.35990456162745]
  We propose a novel multi-view classification algorithm, termed trusted multi-view classification (TMC) TMC provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level. Both theoretical and experimental results validate the effectiveness of the proposed model in accuracy, robustness and trustworthiness.
  arXiv  Detail & Related papers  (2022-04-25T03:48:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.