| Type of Material: | Thesis |
| Title: | A robust deep neural network for image forgery detection |
| Researcher: | Kumar, Sanjeev |
| Guide: | Gupta, Suneet | Gupta, Umesh |
| Department: | School of Computer Science Engineering and Technology |
| Publisher: | Bennett University |
| Place: | Greater Noida |
| Year: | 2022 |
| Language: | English |
| Subject: | Computer Science | Engineering and Technology | Robotics | Computer Science and Information Technology | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; School of Computer Science Engineering and Technology, Bennett University, Greater Noida; 2022 |
| Fulltext: | Shodhganga |
| 003 | IN-AhILN | |
| 005 | 2026-10-12 18:00:49 | |
| 008 | __ | 261012t2022||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_464120 |
| 040 | __ | |dIN-AhILN|aBENN_201310 |
| 041 | __ | |aeng |
| 100 | __ | |eResearcher|aKumar, Sanjeev |
| 110 | __ | |aSchool of Computer Science Engineering and Technology|0U-0890|dGreater Noida|bBennett University|ein |
| 245 | __ | |aA robust deep neural network for image forgery detection |
| 260 | __ | |aGreater Noida|c2022|bBennett University |
| 300 | __ | |axi, 89|dDVD |
| 502 | __ | |cSchool of Computer Science Engineering and Technology, Bennett University, Greater Noida|d2022|bPhD |
| 518 | __ | |oDate of Award|d2022 |
| 518 | __ | |d2017|oDate of Registration |
| 518 | __ | |d2022|oDate of Completion |
| 520 | __ | |aOne of the crucial research areas in the field of image forensics is the detection of image forgeries. Due to the availability of cutting-edge technology, strong image editing tools, and software packages, images can be easily modified or tampered. Human being is living in the world of digitalization. We are surrounded by many images, but all images are not real, it might be fake. There are several of types image forgeries possible, copy-move forgery is one important method, which is in recent trends. In copy move forgery, one part of the image is copied and pasted at different location in the same image, which reflect different perception about the image. To handle this problem, three categories have been used such as block level, key point based and deep learning based solutions. There are various block-level approaches like local binary pattern, stationary wavelet transform, key point-based approaches such as scale invariant feature transform, speedup robust features transform and deep learning based app |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aEngineering and Technology |
| 653 | __ | |aRobotics |
| 700 | __ | |aGupta, Suneet|eGuide |
| 700 | __ | |aGupta, Umesh|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/444952|yShodhganga |
| 905 | __ | |afromsg |
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