Passive detection of image forgery using DCT and local binary pattern

Alahmadi, Amani; Hussain, Muhammad; Aboalsamh, Hatim; Muhammad, Ghulam; Bebis, George; Mathkour, Hassan

doi:10.1007/s11760-016-0899-0

Cited by 112 publications

(102 citation statements)

References 18 publications

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“…Finally, we feed these features to support vector machine (SVM) for learning and classification. Improved classification accuracy over recent methods described in [5] and [6] using four benchmark datasets substantiate the efficacy of our proposed SRIV technique and image forgery detection approach.…”

Section: Introductionsupporting

confidence: 73%

“…Zhang et al [5] and Alahmadi et al [6] proposed their methods utilizing both DCT and LBP. They mainly differ based on the order of DCT and LBP application on image blocks and feature extraction technique.…”

Section: Related Workmentioning

confidence: 99%

“…As a result, active techniques are not commonly observed and practised in today's data driven IoT network. On the other hand, passive approaches (e.g., [5,6]) do not need such prior knowledge, require less resources, and hence have drawn much attentions in digital forensics in recent years. The main idea behind passive (blind) detection is that an altered image might not be visibly identifiable as tampered, but tampering obviously introduces disturbance in the structural and statistical characteristics of an image.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Passive Detection of Splicing and Copy-Move Attacks in Image Forgery

Islam

Karmakar

Murshed

et al. 2018

Neural Information Processing

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Internet of Things (IoT) image sensors for surveillance and monitoring, digital cameras, smart phones and social media generate huge volume of digital images every day. Image splicing and copy-move attacks are the most common types of image forgery that can be done very easily using modern photo editing software. Recently, digital forensics has drawn much attention to detect such tampering on images. In this paper, we introduce a novel feature extraction technique, namely Sum of Relevant Inter-Cell Values (SRIV) using which we propose a passive (blind) image forgery detection method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP). First, the input image is divided into non-overlapping blocks and 2D block DCT is applied to capture the changes of a tampered image in the frequency domain. Then LBP operator is applied to enhance the local changes among the neighbouring DCT coefficients, magnifying the changes in high frequency components resulting from splicing and copy-move attacks. The resulting LBP image is again divided into non-overlapping blocks. Finally, SRIV is applied on the LBP image blocks to extract features which are then fed into a Support Vector Machine (SVM) classifier to identify forged images from authentic ones. Extensive experiment on four well-known benchmark datasets of tampered images reveal the superiority of our method over recent state-of-the-art methods.

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Section: Introductionsupporting

confidence: 73%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Passive Detection of Splicing and Copy-Move Attacks in Image Forgery

Islam

Karmakar

Murshed

et al. 2018

Neural Information Processing

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show abstract

“…En [43] se propone un método que combina el descriptor de textura Local Binary Patterns (LBP) junto a DCT para detectar cambios producidos por las manipulaciones de empalme y también de copia-pega. En los experimentos se utiliza SVM obteniendo una tasa de acierto entre el 97.50 % y el 97.77 % sobre el conjunto de datos "CASIA TIDE v2.0".…”

Section: Iii-b Detección De Empalmeunclassified

Deteccion de Manipulaciones Copy-Move en Ficheros Multimedia mediante la Transformada Discreta del Coseno

Vega¹,

Orozco²,

Villalba³

2020

Seguridad Informática. X Congreso Iberoamericano, CIBSI 2020

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“…Due to its property of texture description, the LBP pattern has been extensively used in face recognition [14] and tamper detection [15]. In [12], the authors introduced LBP operator into watermarking scheme.…”

Section: Lbp Operatormentioning

confidence: 99%

An Improved Secure Semi-fragile Watermarking Based on LBP and Arnold Transform

Zhang¹,

Wang²,

Zhou³

2017

J Inf Process Syst

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In this paper, we analyze a recently proposed semi-fragile watermarking scheme based on local binary pattern (LBP) operators, and note that it has a fundamental flaw in the design. In this work, a binary watermark is embedded into image blocks by modifying the neighborhood pixels according to the LBP pattern. However, different image blocks might have the same LBP pattern, which can lead to false detection in watermark extraction process. In other words, one can modify the host image intentionally without affecting its watermark message. In addition, there is no encryption process before watermark embedding, which brings another potential security problem. To illustrate its weakness, two special copy-paste attacks are proposed in this paper, and several experiments are conducted to prove the effectiveness of these attacks. To solve these problems, an improved semi-fragile watermarking based on LBP operators is presented. In watermark embedding process, the central pixel value of each block is taken into account and Arnold transform is adopted to guarantee the security of watermark. Experimental results show that the improved watermarking scheme can overcome the above defects and locate the tampered region effectively.

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Passive detection of image forgery using DCT and local binary pattern

Cited by 112 publications

References 18 publications

Passive Detection of Splicing and Copy-Move Attacks in Image Forgery

Passive Detection of Splicing and Copy-Move Attacks in Image Forgery

Deteccion de Manipulaciones Copy-Move en Ficheros Multimedia mediante la Transformada Discreta del Coseno

An Improved Secure Semi-fragile Watermarking Based on LBP and Arnold Transform

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