arXiv:2403.14077v1 Announce Type: new
Abstract: DeepFakes, which refer to AI-generated media content, have become an increasing concern due to their use as a means for disinformation. Detecting DeepFakes is currently solved with programmed machine learning algorithms. In this work, we investigate the capabilities of multimodal large language models (LLMs) in DeepFake detection. We conducted qualitative and quantitative experiments to demonstrate multimodal LLMs and show that they can expose AI-generated images through careful experimental design and prompt engineering. This is interesting, considering that LLMs are not inherently tailored for media forensic tasks, and the process does not require programming. We discuss the limitations of multimodal LLMs for these tasks and suggest possible improvements.
Investigating the Capabilities of Multimodal Large Language Models (LLMs) in DeepFake Detection
DeepFakes, which refer to AI-generated media content, have become a significant concern in recent times due to their potential use as a means for disinformation. Detecting DeepFakes has primarily relied on programmed machine learning algorithms. However, in this work, the researchers set out to explore the capabilities of multimodal large language models (LLMs) in DeepFake detection.
When it comes to media forensic tasks, multimodal LLMs are not inherently designed or tailored for such specific purposes. Despite this, the researchers conducted qualitative and quantitative experiments to demonstrate that multimodal LLMs can indeed expose AI-generated images. This is an exciting development as it opens up possibilities for detecting DeepFakes without the need for programming.
One of the strengths of multimodal LLMs lies in their ability to process multiple types of data, such as text and images. By leveraging the power of these models, the researchers were able to carefully design experiments and engineer prompts that could effectively identify AI-generated images. This multi-disciplinary approach combines language understanding and image analysis, highlighting the diverse nature of the concepts involved in DeepFake detection.
However, it is crucial to consider the limitations of multimodal LLMs in these tasks. While they have shown promise, there are still challenges to overcome. For instance, the researchers discuss the need for more extensive datasets that accurately represent the wide range of potential DeepFakes. The current limitations and biases of the available datasets can hinder the performance of these models and limit their real-world applicability.
Furthermore, multimodal LLMs may not be able to detect DeepFakes that have been generated using advanced techniques or by sophisticated adversaries who specifically aim to deceive these models. Adversarial attacks on AI models have been a topic of concern in various domains, and DeepFake detection is no exception. To improve the robustness of multimodal LLMs, researchers should explore adversarial training methods and continuously update the models to stay one step ahead of potential threats.
In conclusion, this work highlights the potential of multimodal large language models in DeepFake detection. By combining the strengths of language understanding and image analysis, these models can expose AI-generated media without the need for programming. However, further research and development are necessary to address the limitations, biases, and potential adversarial attacks. As the field of DeepFake detection continues to evolve, interdisciplinary collaboration and ongoing improvements in multimodal LLMs will play a pivotal role in combating disinformation and safeguarding the authenticity of media content.