Watermarking Framework for Authentication and Self-recovery of Tampered Colour Images

Molina, J.; Ponomaryov, Volodymyr; Reyes-Reyes, Rogelio; Sadovnychiy, Sergiy; Cruz, Clara

doi:10.1109/tla.2020.9082736

Cited by 14 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The distribution of the DCT coefficients is utilized for the localization of tampered regions. Some other techniques are used to detect the traces via median filtering, resampling, contrast enhancement, scaling, and so forth 33‐37 …”

Section: Introductionmentioning

confidence: 99%

“…Some other techniques are used to detect the traces via median filtering, resampling, contrast enhancement, scaling, and so forth. [33][34][35][36][37] However, the availability of versatile photo/video manipulation technologies allows illicit activities such as illegal duplication and deliberate editing of digital files, even though it is difficult to determine whether or not an image has been modified. [38][39][40][41] For the secret revival, parallel or distributed computing methods are widely deployed to overlaps the diverse hares spread over the computer network.…”

mentioning

confidence: 99%

See 1 more Smart Citation

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

Ahmad

Khursheed

2022

Concurrency and Computation

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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

Ahmad

Khursheed

2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…The blind fragile technique of watermarking has been suggested for the detection of image tampering where the watermark embedded in the least significant bit of Red, Green and Blue (RGB) image blocks and the information is retrieved on strong damages (Sinhal et al , 2020) and operations have been placed on non-overlapping and XOR with halftoning (Molina et al , 2020). A semi-blind technique has been proposed to resolve the problem of digital data in terms of copyrights.…”

Section: Introductionmentioning

confidence: 99%

Color image watermarking using spatio-chromatic complex Hadamard transform in sequency domain

Jabeen

Monir

Noor

et al. 2021

WJE

View full text Add to dashboard Cite

Purpose Watermarking technique is one of the significant methods in which carrier signal hides digital information in the form of watermark to prevent the authenticity of the stakeholders by manipulating different coefficients as watermark in time and frequency domain to sustain trade-off in performance parameters. One challenging component among others is to maintain the robustness, to limit perceptibility with embedding information. Transform domain is more popular to achieve the required results in color image watermarking. Variants of complex Hadamard transform (CHT) have been applied for gray image watermarking, and it has been proved that it has better performance than other orthogonal transforms. This paper is aimed at analyzing the performance of spatio-chromatic complex Hadamard transform (Sp-CHT) that is proposed as an application of color image watermarking in sequency domain (SD). Design/methodology/approach In this paper, color image watermarking technique is designed and implemented in SD using spatio-chromatic – conjugate symmetric sequency – ordered CHT. The color of a pixel is represented as complex number a*+jb*, where a* and b* are chromatic components of International Commission on Illumination (CIE) La*b* color space. The embedded watermark is almost transparent to human eye although robust against common signal processing attacks. Findings Based on the results, bit error rate (BER) and peak signal to noise ratio are measured and discussed in comparison of CIE La*b* and hue, saturation and value color model with spatio-chromatic discrete Fourier transform (Sp-DFT), and results are also analyzed with other discrete orthogonal transforms. It is observed from BER that Sp-CHT has 8%–12% better performance than Sp-DFT. Structural similarity index has been measured at different watermark strength and it is observed that presented transform performs better than other transforms. Originality/value This work presents the details and comparative analysis of two orthogonal transforms as color image watermarking application using MATLAB software. A finding from this study demonstrates that the Complex Hadamard transform is the competent candidate that can be replaced with DFT in many signal processing applications.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Due to the technological advancement, potential attackers are now better equipped with various tools to illegally copy, copy-move, retouch, manipulate, or distribute digital data. To alleviate illegal operations, various techniques have been developed that encrypt or watermark the data as a line of defence to provide data confidentiality and copyright protection, respectively, [1][2][3].The development of various image editing technologies has eased the image manipulating while making it difficult to distinguish between altered and natural images [4]. Image tampering methods primarily include image retouching, image morphing, resampling, splicing copy move forgery, image generation, and colourisation [4][5][6].…”

mentioning

confidence: 99%

Dual branch convolutional neural network for copy move forgery detection

Goel

Kaur

Bala

2020

IET Image Processing

View full text Add to dashboard Cite

The advent of digital era has seen a rise in the cases of illegal copying, distribution and forging of images. Even the most secure data channels sometimes suffer to validate the integrity of images. Forgery of multimedia data is devastating in various important applications like defence and satellite. Increased illegal tampering of images has paved way for research in the area of digital forensics. Copy move forgery is one of the various tampering techniques which is used for manipulating an image's content. A deep learning–based passive Copy Move Forgery Detection algorithm is proposed that uses a novel dual branch convolutional neural network to classify images as original and forged. The dual branch convolutional neural network extracts multi‐scale features by employing different kernel sizes in each branch. Fusion of extracted multi‐scale features is then performed to achieve a good accuracy, precision and recall scores. Experiment analysis on MICC F‐2000 dataset has been performed under two different kernel size combinations. Extensive result analysis and comparative analysis proves the efficacy of proposed architecture over existing architecture in terms of performance scores, computation time, and complexity.

show abstract

Watermarking Framework for Authentication and Self-recovery of Tampered Colour Images

Cited by 14 publications

References 0 publications

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

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

Color image watermarking using spatio-chromatic complex Hadamard transform in sequency domain

Dual branch convolutional neural network for copy move forgery detection

Contact Info

Product

Resources

About