Addressing Risks in Text-to-Motion Models

Creating human motions based on text descriptions is becoming a popular area of study. This involves using advanced models that can generate realistic movements from simple text prompts. These techniques are useful in animation, robotics, and human interaction but raise serious safety concerns. If misused, these models could produce dangerous or harmful content.

Despite the focus on developing these Text-to-motion (T2M) models, there hasn’t been much research on protecting them from bad actors. Other fields, like text-to-image (T2I), have made some progress in understanding risks. Still, methods used for images do not fit well with motion generation, which has its own unique challenges.

This article proposes a new method called ALERT-Motion, which uses Large Language Models (LLMs) to create subtle and effective attacks on T2M models. Instead of following fixed rules for changing prompts, ALERT-Motion learns how to craft these prompts on its own. The method consists of two main parts: one that manages the search for better text prompts and another that focuses on gathering relevant motion information. This approach shows promise in generating effective prompts that sound natural and achieve successful results when tested against various T2M models.

#The Importance of Human Motion Generation

Generating human motion is key for many applications, including animation and robotics. As technology improves, models are becoming better at creating natural-looking movements. Text-to-motion models allow users to generate these movements simply by describing them in words, making them very user-friendly.

People are becoming increasingly capable of creating motions that look and feel real, from basic actions to complex sequences. However, this freedom can be dangerous. If someone can use any text to generate movements, it opens the door for misuse. For example, this technology could produce harmful content in movies or animation. With robots possibly acting based on these generated motions, the risks to human safety grow even larger.

Currently, there is little research on how to protect T2M models from malicious use. Most work has centered around text-to-image models. While these studies show that changing certain words can lead to unwanted output, the same tactics cannot be applied easily to motion generation, which involves more complex data.

#The Unique Challenges of T2M Models

One of the main challenges with T2M models is the gap between text and motion. The information in these two areas is represented differently, making it hard to connect them effectively. T2M models need to translate words into physical movements, which involves understanding the nuances of both languages.

Creating prompts that not only fool the model but also maintain natural language is another challenge. The space of potential prompts is vast, making it hard to find the right ones that meet all the necessary criteria. Due to these complexities, generating effective Adversarial Prompts can be difficult.

#Proposed Method: ALERT-Motion

To tackle the challenges of adversarial attacks on T2M models, we introduce ALERT-Motion. This method uses large language models to create effective prompts autonomously. Unlike previous methods, ALERT-Motion relies on LLMs to generate prompts that are subtle, maintaining the integrity of the original text while achieving the desired result in the generated motion.

ALERT-Motion has two main components: the adaptive dispatching module, which guides the search for better prompts, and the multimodal information contrastive module, which gathers relevant information to assist in this process. By combining these two aspects, ALERT-Motion can produce prompts that lead to motions closely resembling the target while remaining difficult to detect.

#How ALERT-Motion Works

ALERT-Motion operates in a black-box setting, meaning it can generate outputs only based on the prompts given, without needing access to the internal workings of the T2M models. The method starts with an initial prompt, generated by ChatGPT, and iteratively refines it using the LLM.

The first step involves generating a variety of prompts that are semantically similar to the original. These prompts are then used to query the T2M model, and the resulting motions are recorded. The LLM uses its reasoning abilities to adjust the prompts based on the outcomes of these queries, refining them until they achieve the desired results.

This approach allows the method to create prompts that not only sound natural but are also closely related to the target motions. The process continues until the generated prompts evade detection while still producing the required outputs from the T2M models.

#Evaluation of ALERT-Motion

To test the effectiveness of ALERT-Motion, we applied it to two widely-used T2M models. We measured its performance against two baseline methods originally designed for text-to-image generation. The results showed that ALERT-Motion outperformed these previous methods in most cases, achieving higher success rates and producing more natural-sounding prompts.

The experiments demonstrated that ALERT-Motion could generate adversarial prompts that closely matched the target motions without being easily recognizable as attacks. This highlights its potential as a valuable tool in understanding and addressing vulnerabilities in T2M models.

#Risks and Safety Concerns

As motion generation technology progresses, the potential for misuse becomes a critical concern. Adversarial attacks carried out through ALERT-Motion could allow harmful content to be generated, which may have serious implications, especially when associated with robotics and automated systems.

The risk of producing explicit or violent content is significant, given that these models may eventually be used in humanoid robots. If proper safety measures are not in place, these robots could engage in dangerous behaviors, posing threats to human safety.

While there hasn't been specific research on defensive measures for T2M models, this work emphasizes the need to develop strategies to mitigate these risks. Current content moderation filters may not be enough to address the vulnerabilities exposed by adversarial attacks.

#Potential Defense Strategies

To counter potential threats, various defense strategies could be considered. For instance, rule-based text filters might struggle against ALERT-Motion, as the adversarial prompts it generates blend seamlessly into normal text, retaining a connection to motion. One possible solution is to train larger datasets to improve the model’s robustness against unexpected prompts.

Additionally, applying techniques from other fields, such as adversarial training, could strengthen T2M models against attacks. This involves training models using both benign and adversarial examples, allowing them to better understand and react to unusual inputs.

#Conclusion

In summary, ALERT-Motion represents a significant step in understanding the vulnerabilities of T2M models. By effectively generating targeted adversarial prompts, it highlights the urgent need for research into defensive measures. As this technology continues to evolve, addressing the potential risks and ensuring the safe deployment of motion generation models will be critical.

The ability of ALERT-Motion to create prompts that achieve specific motion outcomes while remaining subtle shows promise for both understanding and improving T2M systems. However, it also serves as a reminder of the importance of ensuring that these powerful tools are used safely and responsibly in the future. Continued research into both offensive and defensive strategies will be necessary as motion generation technology advances.