Transfer learning effects on image steganalysis with pre-trained deep residual neural network model

Steganalysis researches for the techniques used to reveal the embedded messages that is hidden in a digital medium -in most cases in images. The research and development activities in Image Steganalysis has gained more traction in recent years. Although machine learning techniques have been used for many years Deep Learning is a new paradigm for the Image Steganalysis domain. The success of the deep learning process is based on the training of the model for a sufficient amount of and with a high quality, diverse and large-scale data set. When the training process lacks dataset in terms of quality, variety and quantity, Transfer Learning emerges as an effective solution from Deep Learning methods. In Transfer Learning, an untrained model benefits from a previouslytrainedmodelanditsdataset. Basefunctionisdefinedtotransfertheparameters from the trained model to the untrained model. Hence, it would increase the success of deep learning model on Image Steganalysis. In this work, we compare the results of two series of models that are trained both with and without Transfer Learning method. The optimization method of the model training process is selected as experimental AdamW optimization method. Comparison of training, testing, evaluating and F1 scoring are based on the models trained with different steganography payload values which starts from easy to hard to detect. We investigated for the best possible ways of increasing the success rate and decreasing the error rate on detecting stego images and cover images separately with this study. Results showed that transfer learning applied model is more successful on detecting stego images on every different rated payload dataset compared to the normal trained model.Declaration of Authorship ii Abstract iv Öz v Acknowledgments vii List of Figures x List of Tables xii Abbreviations xiii 1 Introduction 1 1.1 Issue of Secrecy ................................. 1 1.2 Steganography ................................. 3 1.2.1 History of Steganography ....................... 5 1.2.2 A Very Basic Steganographic Method: Least Significant Bit (LSB) 8 1.2.3 An Least Significant Bit (LSB) Example ............... 9 1.3 Steganalysis .................................. 10 1.4 Deep Learning ................................. 11 1.4.1 Convolutional Neural Networks .................... 13 1.4.2 Residual Neural Networks ....................... 15 1.4.3 Transfer Learning ........................... 16 1.5 Contributions ................................. 17 1.6 Outline ..................................... 18 2 Related Work 19 3 Proposed Method: Steganalysis via Transfer Learning 22 3.1 Background ................................... 22 3.2 Transfer Learning Applied Model ....................... 24 3.3 Normal Trained Model ............................. 25 4 Evaluation 27 4.1 Research Questions ............................... 27 4.2 Experimental Setup .............................. 27 4.2.1 The Dataset ............................... 27 4.2.2 Test Environment ........................... 37 4.2.3 Discussions ............................... 38 4.3 Performance Evaluation ............................ 39 5 Results 41 5.1 HUGO Test Results .............................. 42 5.2 WOW Test Results ............................... 48 5.3 Result Comparisons .............................. 52 5.3.1 Train Comparisons ........................... 52 5.3.2 Train Validation Comparisons ..................... 54 5.3.3 Evaluation Comparisons ........................ 56 5.3.4 Prediction Comparisons ........................ 58 5.3.5 Precision Comparisons ......................... 60 5.3.6 Recall Comparisons .......................... 61 5.3.7 F1-Score Comparisons ......................... 65 5.3.8 Related Work Comparisons ...................... 66 6 Conclusion 69 A WOW Training Validation Results 71 B Source Codes 82 Bibliography 9

Convolutional Neural Networks for Image Steganalysis in the Spatial Domain

Esta tesis doctoral muestra los resultados obtenidos al aplicar Redes Neuronales Convolucionales (CNNs) para el estegoanálisis de imágenes digitales en el dominio espacial. La esteganografía consiste en ocultar mensajes dentro de un objeto conocido como portador para establecer un canal de comunicación encubierto para que el acto de comunicación pase desapercibido para los observadores que tienen acceso a ese canal. Steganalysis se dedica a detectar mensajes ocultos mediante esteganografía; estos mensajes pueden estar implícitos en diferentes tipos de medios, como imágenes digitales, archivos de video, archivos de audio o texto sin formato. Desde 2014, los investigadores se han interesado especialmente en aplicar técnicas de Deep Learning (DL) para lograr resultados que superen los métodos tradicionales de Machine Learning (ML).Is doctoral thesis shows the results obtained by applying Convolutional Neural Networks (CNNs) for the steganalysis of digital images in the spatial domain. Steganography consists of hiding messages inside an object known as a carrier to establish a covert communication channel so that the act of communication goes unnoticed by observers who have access to that channel. Steganalysis is dedicated to detecting hidden messages using steganography; these messages can be implicit in di.erent types of media, such as digital images, video €les, audio €les, or plain text. Since 2014 researchers have taken a particular interest in applying Deep Learning (DL) techniques to achieving results that surpass traditional Machine Learning (ML) methods

Image and Video Forensics

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity