Challenges of Face Image Authentication and Suggested Solutions

Salih, Zahraa Aqeel; Thabit, Rasha; Zidan, Khamis A.; Khoo, Bee Ee

doi:10.1109/icitsi56531.2022.9970797

Cited by 3 publications

(2 citation statements)

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“…These techniques can successfully detect specific types of manipulation under specific conditions, but they suffer from some limitations and may face several limitations. Some recently published review papers have been presented to illustrate the types of FIMD, their limitations, and challenges [19]- [23]. The review paper in [23] highlighted some of the challenges that can face FIMD techniques, such as: (a) the rapid development of face image processing applications, (b) the requirement of large and high-quality datasets for training, (c) the need for knowing the type of manipulation applied in order to choose the suitable detection technique, (d) the lack of generalization, (e) the lack of standard metrics, (f) the high complexity and time-consuming process required for training networks, (g) the generation of high-quality fake face images which are difficult to be detected by the trained network, in addition to other challenges.…”

Section: Introductionmentioning

confidence: 99%

“…The review paper in [23] highlighted some of the challenges that can face FIMD techniques, such as: (a) the rapid development of face image processing applications, (b) the requirement of large and high-quality datasets for training, (c) the need for knowing the type of manipulation applied in order to choose the suitable detection technique, (d) the lack of generalization, (e) the lack of standard metrics, (f) the high complexity and time-consuming process required for training networks, (g) the generation of high-quality fake face images which are difficult to be detected by the trained network, in addition to other challenges. Some solutions have been suggested to overcome the limitations and challenges [23]. One suggested strategy is to implement a face image authentication technique based on image watermarking.…”

Section: Introductionmentioning

confidence: 99%

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A New Face Region Recovery Algorithm based on Bicubic Interpolation

Al-Hadaad,

Thabit,

Zidan

2023

JOIV : Int. J. Inform. Visualization

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Recently, researchers focused on face image manipulation detection and localization techniques because of their importance in image security applications. The previous research has not highlighted the recovery of the face region after manipulation detection. This paper presents a new face region recovery algorithm (FRRA) to be included in the face image manipulation detection algorithms (FIMD). The proposed FRRA consists of two main algorithms: face data generation algorithm and face region restoration algorithm. Both algorithms start by detecting the face region using Multi-task Cascaded Neural Network followed by a face window selection process. In the face data generation algorithm, the recovery information is generated from the shirked face window using bicubic interpolation technique. In the face region restoration algorithm, the face region zoomed using bicubic interpolation technique. The proposed FRRA has been tested and compared with previous recovery methods for different color face images, and the results proved that the FRRA could recover the face region with better visual quality at the same data length compared to previous methods. The main contributions of this research are a) the suggestion of including a face region recovery algorithm to FIMD, b) the study of previous recovery data generation algorithms for color face images, and c) introducing a new algorithm for generating the recovery data based on bicubic interpolation. In the future, the proposed algorithm can be included in the recent FIMD algorithms to recover the face region, which can be very useful in practical applications, especially those used in data forensics systems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%