Efficient hybrid framework for transmission enhancement of composite 3D H.264 and H.265 compressed video frames

El-Bakary, E. M.; El‐Shafai, Walid; El-Rabaie, El-Sayed M.; Zahran, O.; El‐Samie, Fathi E. Abd

doi:10.1007/s11042-018-6464-9

Cited by 2 publications

(1 citation statement)

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“…Image enhancement methods can be classified into spatial-domain and transform-domain methods [35][36][37][38]. Spatial-domain methods depend on the direct manipulation of the pixels in the image.…”

Section: Related Workmentioning

confidence: 99%

Efficient Forgery Detection Approaches for Digital Color Images

Baumy¹,

Algarni²,

Abdalla³

et al. 2022

Computers, Materials &Amp; Continua

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This paper is concerned with a vital topic in image processing: color image forgery detection. The development of computing capabilities has led to a breakthrough in hacking and forgery attacks on signal, image, and data communicated over networks. Hence, there is an urgent need for developing efficient image forgery detection algorithms. Two main types of forgery are considered in this paper: splicing and copy-move. Splicing is performed by inserting a part of an image into another image. On the other hand, copy-move forgery is performed by copying a part of the image into another position in the same image. The proposed approach for splicing detection is based on the assumption that illumination between the original and tampered images is different. To detect the difference between the original and tampered images, the homomorphic transform separates the illumination component from the reflectance component. The illumination histogram derivative is used for detecting the difference in illumination, and hence forgery detection is accomplished. Prior to performing the forgery detection process, some pre-processing techniques, including histogram equalization, histogram matching, high-pass filtering, homomorphic enhancement, and single image super-resolution, are introduced to reinforce the details and changes between the original and embedded sections. The proposed approach for copy-move forgery detection is performed with the Speeded Up Robust Features (SURF) algorithm, which extracts feature points and feature vectors. Searching for the copied partition is accomplished through matching with Euclidian distance and hierarchical clustering. In addition, some preprocessing methods are used with the SURF algorithm, such as histogram equalization and single-mage super-resolution. Simulation results proved the feasibility and the robustness of the pre-processing step in homomorphic detection and SURF detection algorithms for splicing and copy-move forgery detection, respectively.

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Section: Related Workmentioning

confidence: 99%