Blind Image Watermarking for Localization and Restoration of Color Images

Sinhal, Rishi; Ansari, Irshad Ahmad; Ahn, Chang Wook

doi:10.1109/access.2020.3035428

Cited by 36 publications

(16 citation statements)

References 30 publications

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“…There are some recent papers that claim that their methods can recover the original image, after tampering rates of more than 50% [23][36] [37], but the required quality of their recovered images is not satisfied. For example, in [23] [36] the quality of their recovered images is less than 30dB even when the tampering rate is only around 30%. It is obvious for any method, the greater the tampering rate results in reduced quality for the recovered image, therefore the criteria are not met with their method beyond 30% tampering.…”

Section: Background Reviewmentioning

confidence: 99%

Detection and Recovery of Higher Tampered Images Using Novel Feature and Compression Strategy

2021

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Due to the availability of powerful image-editing software and the growing amount of multimedia data that is transmitted via the Internet, integrity verifications and confidentiality of the data are becoming critical issues. However, currently, the accuracy of detecting and the recovery capability of the tampered images by the existing methods through watermarking strategy is still not at the required level, especially at a higher tampered rate. This paper proposes a new blind and fragile watermarking method to detect tampering and better recovery of tampered images. To improve the quality of both the watermarked and the recovered images, a new feature extraction scheme is introduced which will produce a short but comprehensive recovery code using a new compression strategy. If a block in the image tampers, the proposed embedded feature allows the original data to be extracted for recovery. To overcome tamper coincidence, every block's watermarked data contains not only the recovery code belonging to the block itself but also its neighbor's data as a second layer of recovery. Various size blocks were investigated to see the performance and compare their efficiency for recovering an image after different tampering rates. The test showed the smaller block sizes may be more suitable for locating tampering, where the bigger ones are more suitable when the tampering rate is higher. The bigger block sizes in the proposed method can recover an image even after a 60% tampering rate with high quality (more than 31 dB). The experimental results prove that the proposed method can have better efficiency for detecting tampering, and recovery of the original image, compared to the relevant existing methods.

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Section: Background Reviewmentioning

confidence: 99%

Detection and Recovery of Higher Tampered Images Using Novel Feature and Compression Strategy

2021

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“…Sinhal et al [10]. proposed an effective blind extraction and easy-to-detect watermarking scheme for color images.…”

Section: Related Workmentioning

confidence: 99%

Blind Extraction of Digital Watermarking Algorithm for Color Images

Gong

Wang

2021

SSRN Journal

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Digital watermark technology hides copyright information in digital images, effectively protecting the copyright of digital images. At present, the color image digital watermarking algorithm still has defects such as the inability to balance robustness, invisibility and the weak anti-attack ability. Aiming at the above problems, this paper studies the digital watermarking method based on discrete wavelet transform and discrete cosine transform. Then this paper proposes a color image blind digital watermarking algorithm based on QR code. First, convert the color image from RGB space to YCbCr space, extract the Y component and perform the second-level discrete wavelet transform. secondly, block the LL2 subband and perform the discrete cosine transform. finally, use the embedding method to convert the watermark information after the Arnold transform embedded in the block. The experimental results show that the PSNR of the color image embedded with the QR code is 56.7159 without being attacked. After being attacked, its PSNR and NC values are respectively 30dB and 0.95 or more, which proves that the algorithm has good robustness and can achieve watermarking blind extraction.

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“…Some techniques execute tampering detection at image level or patch level with CNN and then subjected for localization of tampering through sliding window analysis. Localization of Pixel or region‐level tampering has been proposed in recent days 27‐31 . Some of the techniques are based on discrete network structures, such as FCN, RCNN, LSTM, and so forth.…”

Section: Introductionmentioning

confidence: 99%

“…Localization of Pixel or region-level tampering has been proposed in recent days. [27][28][29][30][31] Some of the techniques are based on discrete network structures, such as FCN, RCNN, LSTM, and so forth. A method known as Visual cryptography is primarily an addition of the concept called secret sharing.…”

mentioning

confidence: 99%

Detection and localization of image tampering in digital images with fused features

Ahmad

Khursheed

2022

Concurrency and Computation

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Summary In digital forensics, image tamper detection and localization have attracted increased attention in recent days, where the standard methods have limited description ability and high computational costs. As a result, this research introduces a novel picture tamper detection and localization model. Feature extraction, tamper detection, as well as tamper localization are the three major phases of the proposed model. From the input digital images, a group of features like “Scale‐based Adaptive Speeded Up Robust Features (SA‐SURF), Discrete Wavelet Transform (DWT) based Patched Local Vector Pattern (LVP) features, HoG feature with harmonic mean based PCA and MBFDF” are extracted. Then, with this extracted feature strain the “optimized Convolutional Neural Network (CNN)” will be trained in the tamper detection phase. Since it is the key decision‐maker about the presence/absence of tamper, its weighting parameters are fine‐tuned via a novel improved Sea‐lion Customized Firefly algorithm (ISCFF) model. This ensures the enhancement of detection accuracy. Once an image is recognized to have tampers, then it is essential to identify the tamper localization. In the tamper localization phase, the copy‐move tampers are localized using the SIFT features, splicing tampers are localized using the DBN and the noise inconsistency is localized with a newly introduced threshold‐based tamper localization technique. The simulation outcomes illustrate that the adopted model attains better tamper detection as well as localization performance over the existing methods.

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Blind Image Watermarking for Localization and Restoration of Color Images

Cited by 36 publications

References 30 publications

Detection and Recovery of Higher Tampered Images Using Novel Feature and Compression Strategy

Detection and Recovery of Higher Tampered Images Using Novel Feature and Compression Strategy

Blind Extraction of Digital Watermarking Algorithm for Color Images

Detection and localization of image tampering in digital images with fused features

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