Source URL: https://slashdot.org/story/25/08/12/2214243/cornell-researchers-develop-invisible-light-based-watermark-to-detect-deepfakes?utm_source=rss1.0mainlinkanon&utm_medium=feed
Source: Slashdot
Title: Cornell Researchers Develop Invisible Light-Based Watermark To Detect Deepfakes
Feedly Summary:
AI Summary and Description: Yes
Summary: Researchers at Cornell University have developed an innovative watermarking system based on coded light, enhancing the detection of deepfakes through a method that requires no special hardware. This system offers a more resilient verification process compared to traditional watermarking techniques, which is especially significant for professionals in AI and information security.
Detailed Description:
The breakthrough at Cornell University introduces a novel approach to watermarking that harnesses light, a critical aspect of both video recording and analysis. This system embeds unique codes into light used during video recording, allowing for efficient detection of manipulations, such as deepfakes.
Key points include:
– **Invisible Watermarking**: The system uses coded light patterns that can be captured by any camera, enabling authentication without specialized equipment.
– **Deepfake Detection**: By analyzing these codes, analysts can identify alterations in videos, addressing the rising concern of deepfake technologies.
– **Adaptable Technology**: Programmable light sources (like monitors or studio lights) can integrate coded brightness patterns through software, while standard lamps can be modified with a small chip to create variability in light intensity.
– **Human Perception Consideration**: The coding is informed by research in human visual perception, ensuring the codes remain undetectable to the naked eye.
– **Time-stamped Records**: Each lighting code acts as a low-resolution time-stamped record, differentiating between original footage and manipulated content.
– **Complex Watermarking**: The system allows for the embedding of multiple independent lighting codes in a single scene, increasing complexity and thwarting forgery attempts.
– **Detection of Manipulations**: Analysts can detect missing sequences or altered scenes by comparing the original footage with the generated code videos, revealing gaps or discrepancies.
– **Presentation**: This concept, known as noise-coded illumination, was presented at SIGGRAPH 2025, highlighting its potential within the fields of computer vision and video security.
This research is crucial for fields related to AI security, visual content verification, and compliance with digital content standards as it enhances the resilience against digital forgery and bolsters the integrity of shared media.