Unveiling Hidden Vulnerabilities in Digital Human Generation via Adversarial Attacks

2025-04-24

AI Summary

This work exposes a critical security vulnerability in expressive human pose and shape (EHPS) estimation models widely used in digital human generation: while existing methods prioritize estimation accuracy, they largely neglect robustness and adversarial resilience. To address this gap, we propose Tangible Attack (TBA), a novel framework featuring a dual heterogeneous noise generator (DHNG) and a customized adversarial loss function, integrated with VAE-based latent modeling, ControlNet conditioning, and multi-gradient iterative optimization. TBA enables cross-model, highly controllable, and strongly disruptive targeted adversarial attacks. Experiments demonstrate that TBA increases EHPS estimation error by an average of 17.0% and up to 41.0%, providing the first systematic evidence of severe security risks in mainstream digital human generation systems. Our work establishes a vital benchmark for evaluating model robustness and offers concrete directions for enhancing reliability and trustworthiness in expressive human modeling.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Expressive human pose and shape estimation (EHPS) is crucial for digital human generation, especially in applications like live streaming. While existing research primarily focuses on reducing estimation errors, it largely neglects robustness and security aspects, leaving these systems vulnerable to adversarial attacks. To address this significant challenge, we propose the extbf{Tangible Attack (TBA)}, a novel framework designed to generate adversarial examples capable of effectively compromising any digital human generation model. Our approach introduces a extbf{Dual Heterogeneous Noise Generator (DHNG)}, which leverages Variational Autoencoders (VAE) and ControlNet to produce diverse, targeted noise tailored to the original image features. Additionally, we design a custom extbf{adversarial loss function} to optimize the noise, ensuring both high controllability and potent disruption. By iteratively refining the adversarial sample through multi-gradient signals from both the noise and the state-of-the-art EHPS model, TBA substantially improves the effectiveness of adversarial attacks. Extensive experiments demonstrate TBA's superiority, achieving a remarkable 41.0% increase in estimation error, with an average improvement of approximately 17.0%. These findings expose significant security vulnerabilities in current EHPS models and highlight the need for stronger defenses in digital human generation systems.

Problem

• Exposing security vulnerabilities in digital human generation models
• Addressing robustness gaps in expressive human pose estimation
• Developing adversarial attacks to test EHPS model defenses

Innovation

• Proposes Tangible Attack (TBA) for adversarial examples
• Uses Dual Heterogeneous Noise Generator (DHNG) with VAE
• Custom adversarial loss function for optimized noise