ABSTRAT:
In this paper, we investigate whether robust hashing has a possibility to robustly detect fake-images even when multiple manipulation techniques such as JPEG compression are applied to images for the first time. In an experiment, the proposed fake detection with robust hashing is demonstrated to outperform state-of-the-art one under the use of various datasets including fake images generated with GANs.
EXISTING SYSTEM :
Fake images are manually generated by using image editing tools such as Photoshop. Splicing, copy-move, and deletion are also carried out under the use of such a tool. Similarly, resizing, rotating, blurring, and changing the colour of an image can be manually carried out. In addition, recent rapid advances in deep image synthesis techniques such as GANs have automatically generated fake images. Cycle GAN [10] and Star GAN [11] are typical image synthesis techniques with GANs. Cycle GAN is a GAN that performs one-to-one transformations, e.g. changing apples to oranges, while Star GAN is a GAN that performs many-to- many transformations, such as changing a person’s facial expression or hair colour (see Figs.1 and 3). Furthermore, fake videos created using deep learning are called Deepfake, and various tampering methods have emerged, such as those using autoencoders, Face2Face
EXISTING SYSTEM DISADVANTAGES:
1.LESS ACCURACY
2. LOW EFFICIENCY
PROPOSED SYSTEM :
how an overview of the proposed method. In the framework, robust hash value is computed from easy reference image by using a robust hash method, and stored in a database. Similar to reference images, a robust hash value is computed from a query one by using the same hash method. The hash value of the query is compared with those stored the database. Finally, the query image is judged whether it is real or fake in accordance with the distance between two hash values.
PROPOSED SYSTEM ADVANTAGES:
1.HIGH ACCURACY
2.HIGH EFFICIENCY
SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10
HARDWARE REQUIREMENTS:
Processor : Core I3
RAM Capacity : 2 GB
Hard Disk : 250 GB
Monitor : 15″ Color
Mouse : 2 or 3 Button Mouse
Key Board : Windows 08/10
