🤹♂️AI Image
Last updated
Last updated
The advanced concept of Layly is further enhanced through our Large Language Models (LLMs) system, which incorporates natural language processing capabilities to improve the detection of deepfakes and fake content.
Using sophisticated algorithms and deep learning, Layly not only recognizes visual alterations but also analyzes contexts and captions associated with images to identify inconsistencies that may suggest manipulation.
Additionally, we incorporate metadata analysis and cryptographic hashing techniques to verify the provenance and authenticity of multimedia files, offering a comprehensive and cutting-edge solution to ensure information accuracy in the digital age.
Loss: 0.163
Accuracy: 0.942
Precision: 0.938
Recall: 0.978
AUC: 0.980
F1: 0.958
To enhance our Large Language Models (LLMs), we have expanded our training to include the classification of a variety of images generated by advanced AI techniques such as VQGAN+CLIP, Disco Diffusion, DALL-E 2, Midjourney, and Craiyon.
Additionally, we have incorporated insights from cutting-edge scientific research, as reflected in the study on the implementation and application of the Swin Transformer in image classification, available on ResearchGate. This multifaceted approach not only improves the accuracy of our models in detecting falsified content but also enriches their capability to understand and process visual complexities across various contexts.
In 2020, Kaggle successfully hosted a competition aimed at detecting AI-generated deepfakes, achieving significant progress in this critical area of cybersecurity.
Image classification models are employed to recognize an impressive array of items, from detecting plant diseases to identifying NSFW content in online image posts.
Using the Reddit Downloader tool, we have streamlined the collection of thousands of images from art and photography subreddits to efficiently train our Large Language Models (LLMs).
Training the model with this dataset proved to be quick and straightforward due to the implementation of the FastAI library, achieving a model that reached an approximate accuracy of 80%
In our strategic plan, we aim to enhance our image analysis capabilities using Stable Diffusion. We plan to create a substantially larger training dataset using Open Prompts, which includes 10 million generated messages and images.
These have been instrumental in developing platforms like krea.ai and lexica.art. Additionally, we will incorporate subsets of LAION data that have been crucial for training the Stable Diffusion model. This approach will enable us to develop a highly robust image classification model based on millions of images.
Currently, our Large Language Model (LLMs) for images is still in the training phase, utilizing GPU resources through Amazon AWS services, specifically using SageMaker.
While the code is not yet released for public use, we plan to make it open source in the coming months, thereby facilitating access and collaboration within the developer community
