Robust Detection Of AI-Generated Fake Images Using Deep Learning

The proposed approach to the detection of AIgenerated images based on deep learning has been introduced and discussed in this study. The system makes use of the image preprocessing techniques and a convolutional neural network model to categorize the pictures into real and synthetic. Through visual patterns and structural features analysis of images, the suggested system will be capable of detecting even minor variations between original photographs and AI-generated images. The results of the experiment support the claim that deep learning methods can be incredibly effective in identifying AI-generated images. The trained CNN model could predict high classification accuracy because it detected the artifacts and inconsistencies that were in the synthetic images that were hidden. The proposed system offers a viable idea towards enhancing the authenticity and reliability of digital visual content. This type of systems can be significantly involved in the avoidance of misinformation, manipulation of digital data, and the reliability of online media. To advance the work in the future, the system may be enhanced by training the model to work with larger and more diverse datasets. Also, more complicated deep learning designs and a composite detection method can be considered to improve the model to detect more lifelike images produced by AI. The trained CNN model could predict high classification accuracy because it detected the artifacts and inconsistencies that were in the synthetic images that were hidden.

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