https://www.loom.com/share/cb6b91c7e55945a7ad4526409e667296?sid=5c783c16-10b2-4784-8570-161cdfbe4fd0

Presented by Deep Media AI (See our other research in Deepfake Detection here)

Published: 2024 - 07 - 01

Updated: 2024 - 08 - 15

Dataset ID: 240705

deepmedia-analysis-toolkit-public

Unveiling The World’s Largest Public Benchmark for High-Fidelity GenAI Image Detection

3D Clustering of CLIP Embeddings Across Known GenAI and “Unmanipulated” Images

3D Clustering of CLIP Embeddings Across Known GenAI and “Unmanipulated” Images

REAL

REAL

GENAI

GENAI

Download This Dataset

Fill out this form with your information and someone from the Deep Media research term will grant you access: https://docs.google.com/forms/d/e/1FAIpQLSeTupVdPJDJM6hAEYDCD5ndIu1lvqZhc_xR_woC5hike532hw/viewform?usp=sf_link

Our Academic Research Partners

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Abstract

Generative AI can create hyper-realistic images at unprecedented speed and scale. The threat of GenAI Disinformation (ie “Deepfakes”) has evolved from a theoretical concern to an urgent societal challenge. As a pioneer in Deepfake Detection and AI Disinformation Security, Deep Media is at the forefront of combating this digital disinformation crisis.

In an effort aid our government and academic research partners, we are thrilled to introduce our new Deepfake Detection In-The-Wild Benchmark dataset for GENAI Image detection.

We invite researchers, institutions, and tech platforms to leverage this dataset in pushing the boundaries of synthetic media detection. Together, we can develop the next generation of safeguards against digital deception and preserve the integrity of our shared information ecosystem.

The future of AI security starts here.

Coming Soon: Expanding the frontier with voice and video-face datasets. The AI arms race is evolving – and so are we.

CAN YOU SPOT THE FAKES?

Download 100 sample images and test your skills:

highlighted-images.zip

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Advanced Image Embedding Analysis and Visualizations

In the rapidly evolving landscape of AI-generated content, Deep Media leverages cutting-edge unsupervised learning techniques to stay ahead of emerging threats. Our approach combines sophisticated CLIP-embedding clustering with innovative visualization methods, setting a new standard in analyzing deepfake detection.

Adaptive K-Means Clustering

To uncover hidden patterns in our diverse dataset, we employ unsupervised machine learning techniques. The challenge lies in determining the optimal number of clusters (K) that best represents the data's structure.

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Utilizing the unsupervised elbow method ensures our analysis scales seamlessly with the exponential growth of AI-generated content, adapting to new patterns and complexities in real-time.

Cluster Visualization to Aid Data Science and Human Intuition

Deep Media's unique approach to cluster visualization represents a significant advancement in making complex AI analysis accessible and actionable.

clusters_closest_images.jpg

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This approach bridges the gap between complex AI models and human intuition, enabling:

For data scientists and researchers, integrating this dataset and visualization technique into their workflow allows for:

This, in turn, allows us to develop more sophisticated, context-aware detection systems that can adapt to the ever-evolving challenges of synthetic media.

By open-sourcing this approach, Deep Media is setting a new standard in the field, encouraging collaboration and innovation in the fight against digital misinformation.

PCA Clustering and Visualizations: Unveiling the Hidden Structure of Synthetic Media

2D PCA Visualization: A Window into High-Dimensional Data

Principal Component Analysis (PCA) is a cornerstone technique in dimensionality reduction, crucial for understanding complex datasets like those in Deepfake detection. By projecting high-dimensional CLIP embeddings onto a 2D plane, we create an intuitive visual representation of our data's structure.

For data scientists, these 2D PCA visualizations serve as a powerful exploratory tool. They reveal clusters, outliers, and patterns that might be invisible in the raw data. In the context of Deepfake detection, these visualizations can highlight separations between genuine and synthetic images, showcase the diversity of AI-generated content, and identify challenging edge cases that blur the lines between real and fake.

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By color-coding points based on their known labels (real or synthetic) and overlaying cluster centroids, we create a rich visual narrative. This allows researchers to quickly assess the effectiveness of current detection models, identify areas where the model may be struggling, and guide feature engineering efforts. For instance, tight clusters of misclassified images might indicate a specific type of Deepfake that current models find challenging, prompting focused research in that area.

3D PCA Visualizations

While 2D visualizations are invaluable, Deep Media pushes the boundaries by implementing 3D PCA visualizations. This approach retains an additional dimension of information, potentially uncovering relationships masked in the 2D projection. Our innovative technique doesn't just stop at static 3D plots; we've developed a dynamic, multi-angle visualization system that offers unprecedented insights into the data structure.

The power of this approach lies in its ability to reveal relationships that might be obscured from any single viewpoint. As the perspective shifts, clusters that seemed distinct might merge, while seemingly homogeneous regions might separate. This dynamic view is particularly crucial in Deepfake detection, where the boundaries between real and synthetic content can be subtle and multifaceted.

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By providing multiple 3D perspectives, we enable researchers to:

  1. Identify complex, non-linear decision boundaries between real and fake images
  2. Discover subgroups within the broader categories of real and synthetic media
  3. Visualize the trajectory of Deepfake technology evolution by plotting samples from different generation algorithms or time periods

This advanced visualization technique not only enhances our understanding of current Deepfake detection challenges but also guides the development of more sophisticated, multi-dimensional detection strategies. It allows us to move beyond binary classification, paving the way for nuanced, context-aware detection systems that can adapt to the ever-evolving landscape of synthetic media.

Conclusion: Pioneering the Future of Deepfake Detection

Deep Media's latest advancements in deepfake detection technology represent a significant leap forward in the fight against digital misinformation. Our results demonstrate: