Enhancing image authenticity: A new approach for binary fake image classification using DWT and swin transformer

The rise of social media and the easy sharing of images has led to a big increase in fake photos, making it tough to trust what we see. The proposed study, in a constructive way, presents a method of distinguishing fake from real images by the Swin Transformer and DWT (Discrete Wavelet Transform) correctly and efficiently. To capture the most features, the Swin Transformer processes the data on various levels. DWT can segment images into several frequency components required to recover different objects. It can also create a map containing all the sudden changes or discrepancies between fake images and real images by drawing features along edges and textures; It produces features across everything around the local image that are vital for detecting these things. This model is very good at tolerating noise and compression errors, making it applicable to various types of images or image manipulation. However, It focuses on local details and spatial changes. It can also pre-process and down sample image data before feeding it into Swin Transformer; possibly reducing the processing demands of the model. Using this self-attention mechanism allows the Swin Transformer to understand how different parts of an image are connected and what’s going on in a bigger context. That knowledge base allows it to identify strange or unnatural patterns in its own edited photos, such as discolored areas and certain textures. Due to the hierarchical architecture of the system, it is quickly able to comprehend compounded image details and relationships which as a result has a better capability of discriminating between real and manipulated images. Putting it simply, we could come up with an attractive way to identify fake images from real ones by combining Wavelet transform and the Swin Transformer approach. This process can increase the extraction of features improve the ability and reliability of the model reduce complexity. It is an enabling technique to produce further effective solutions against the emerging problem of image manipulation and deceit in digital media. "Our research provides a strong foundation for detecting altered images and digital media, with an accuracy rate greater than 91% accuracy rate and surpassing current techniques. It can even detect minor image alterations, providing a dependable solution for digital image fraud.