Hybrid Algorithm for the detection of Pixel-based digital image forgery using Markov and SIFT descriptors

Revista Facultad de Ingeniería, Universidad de Antioquia, No.105, pp. 111-121, Oct-Dec 2022 Hybrid algorithm for the detection of Pixel-based digital image forgery using Markov and SIFT descriptors Algoritmo híbrido mediante descriptores Markov y SIFT para la detección de la falsificación de imágenes Jimmy Alexander Cortés-Osorio 1* , José Andrés Chaves-Osorio 1 , Cristian David López-Robayo 1 1 Facultad de Ciencias Básicas, Universidad Tecnológica de Pereira. Carrera 27 # 10-02. C. P. 660003. Pereira, Risaralda ABSTRACT: Today, CITE THIS ARTICLE AS: J. A. Cortés, J. A. Chaves and C. D. López. ”Hybrid Algorithm for the detection of Pixel-based digital image forgery using Markov and SIFT descriptors”, Revista Facultad de Ingeniería Universidad de Antioquia, no. 105, pp. 111-121, Oct-Dec 2022. [Online]. Available: https: //www.doi.org/10.17533/ udea.redin.20211165 ARTICLE INFO: Received: January 30, 2021 Accepted: October 29, 2021 Available online: November 02, 2021 KEYWORDS: Copy-Move; Markov; Resampling; SIFT; Splicing Copiar-Mover; Markov; Remuestreo; SIFT; Empalme image forgery is common due to the massification of low-cost/high-resolution digital cameras, along with the accessibility of computer programs for image processing. All media is affected by this issue, which makes the public doubt the news. Though image modification is a typical process in entertainment, when images are taken as evidence in a legal process, modification cannot be considered trivial. Digital forensics has the challenge of ensuring the accuracy and integrity of digital images to overcome this issue. This investigation introduces an algorithm to detect the main types of pixel-based alterations such as copy-move forgery, resampling, and splicing in digital images. For the evaluation of the algorithm, CVLAB, CASIA V1, Columbia, and Columbia Uncompressed datasets were used. Of 7100 images evaluated, 3666 were unaltered, 791 had resampling, 2213 had splicing, and 430 had copy-move forgeries. The algorithm detected all proposed forgery pixel methods with an accuracy of 91%. The main novelties of the proposal are the reduced number of features needed for identification and its robustness for the file format and image size. RESUMEN: Hoy en día, la falsificación de imágenes es común debido a la masificación de las cámaras digitales de alta resolución y bajo costo, junto con la accesibilidad de los programas de computadora para el procesamiento de imágenes. Todos los medios de comunicación se ven afectados por este tema, lo que hace que el público dude de la noticia. Aunque la modificación de imágenes es un proceso común en el entretenimiento, cuando las imágenes se toman como evidencia en un proceso legal, la alteración no puede considerarse trivial. La ciencia forense digital tiene el desafío de garantizar la precisión y la integridad de las imágenes digitales para superar este problema. Esta investigación introduce un algoritmo para detectar los principales tipos de alteraciones basadas en píxeles, como copy-move, resamplig y splicing en imágenes digitales. Para la evaluación del algoritmo se utilizaron las bases de datos CVLAB, CASIA V1, Columbia y Uncompressed Columbia. Se evaluaron 7.100 imágenes, de las cuales 3666 eran auténticas, 791 tenían resampling, 2213 tenían splicing y 430 tenían falsificaciones de copy-move. El algoritmo detectó todas las alteraciones basadas en pixeles con una precisión del 91%. Las principales novedades de la propuesta son el reducido número de características necesarias para la identificación y su robustez al formato y tamaño de la imagen. 1. Introduction * Corresponding author: Jimmy Alexander Cortés-Osorio E-mail: ISSN 0120-6230 Image manipulation is more common today due to the massification of low-cost/high-resolution cameras, along with the availability of programs for image processing such as Inkscape, Photoshop, and Corel Draw, among others. Although image alteration is common in entertainment, when images are taken as evidence in a legal process maintaining the integrity of the original image is fundamental. Thus, digital forensics has e-ISSN 2422-2844 111 DOI: 10.17533/udea.redin.20211165 111 J. A. Cortés-Osorio et al., Revista Facultad de Ingeniería, Universidad de Antioquia, No. 105, pp. 111-121, 2022 the challenge of ensuring the accuracy and integrity of images in a legal process. Various researchers have made significant efforts to identify manipulated images using digital image processing algorithms. This is because the process of visually identifying alterations to digital images is complicated for the human eye. Researchers have found that the process of falsifying an image modifies the neighborhood and statistics of the host image [1]. Thanks to these traces of alterations, it is possible to detect altered images. The scientific literature describes different approaches to detect the falsification of digital images, including methods based on the type of camera, as well as the format, physics, geometry, and pixels of the image [2]. One of the main advantages of pixel-based methods is that they do not require knowledge of either the camera manufacturer’s parameters or the original image. 112 with SIFT on the detail coefficients. Subsequently, they compared the feature vectors to identify which regions were falsified. Another recognized feature extractor is the Speeded Up Robust Features (SURF), which detects interest points and descriptors [8, 9]. A technique to detect copy-move forgeries based on SURF and KD-Tree for the comparison of multidimensional data was presented in [10]. Among the most common alterations in pixel-based methods are copy-move, resampling, and splicing. According to [3], copy-move alteration takes place when a region of an image is copied and pasted into the same image without any geometric transformation. In contrast, if at the time of pasting the copied region some geometric transformation is performed, this is known as resampling. Splicing is generated by copying a region of an image and pasting it in a different one to add or hide important information. Although these operations may be visually imperceptible, it is possible to find statistical changes in the image due to a correlation change between neighboring pixels at the edges of the base image [2]. Recently, many researchers applied deep learning methods in areas such as mechanics [11], medicine [12], and a solution for copy-move detection [13]. [14] proposed a deep learning approach with a transfer learning model that uses VGG-16 custom design convolutional neural network (CNN). A deep learning technique based on the CNN model with multi-scale-input and multi-stages of the convolutional layer was proposed [15]. This method used three phases: encoder block, decoder block to extract feature maps and classification. In the encoder block, the images are downsampled in multiple levels to extract features maps. In the decoder block, features map (...truncated)