Research on Improved MobileViT Image Tamper Localization Model

As image manipulation technology advances rapidly,the malicious use of image tampering has alarmingly escalated,posing a significant threat to social stability.In the realm of image tampering localization,accurately localizing limited samples,multiple types,and various sizes of regions remains a multitude of challenges.These issues impede the model’s universality and generalization capability and detrimentally affect its performance.To tackle these issues,we propose FL-MobileViT-an improved MobileViT model devised for image tampering localization.Our proposed model utilizes a dual-stream architecture that independently processes the RGB and noise domain,and captures richer traces of tampering through dual-stream integration.Meanwhile,the model incorporating the Focused Linear Attention mechanism within the lightweight network(MobileViT).This substitution significantly diminishes computational complexity and resolves homogeneity problems associated with traditional Transformer attention mechanisms,enhancing feature extraction diversity and improving the model’s localization performance.To comprehensively fuse the generated results from both feature extractors,we introduce the ASPP architecture for multi-scale feature fusion.This facilitates a more precise localization of tampered regions of various sizes.Furthermore,to bolster the model’s generalization ability,we adopt a contrastive learning method and devise a joint optimization training strategy that leverages fused features and captures the disparities in feature distribution in tampered images.This strategy enables the learning of contrastive loss at various stages of the feature extractor and employs it as an additional constraint condition in conjunction with cross-entropy loss.As a result,overfitting issues are effectively alleviated,and the differentiation between tampered and untampered regions is enhanced.Experimental evaluations on five benchmark datasets(IMD-20,CASIA,NIST-16,Columbia and Coverage)validate the effectiveness of our proposed model.The meticulously calibrated FL-MobileViT model consistently outperforms numerous existing general models regarding localization accuracy across diverse datasets,demonstrating superior adaptability.

An Overview of Image Tamper Detection

With the popularization of high-performance electronic imaging equipment and the wide application of digital image editing software,the threshold of digital image editing becomes lower and lower.Thismakes it easy to trick the human visual system with professionally altered images.These tampered images have brought serious threats to many fields,including personal privacy,news communication,judicial evidence collection,information security and so on.Therefore,the security and reliability of digital information has been increasingly concerned by the international community.In this paper,digital image tamper detection methods are classified according to the clues that they rely on,detection methods based on image content and detection methods based on double JPEG compression traces.This paper analyzes and discusses the important algorithms in several classification methods,and summarizes the problems existing in various methods.Finally,this paper predicts the future development trend of tamper detection.

An Active Image Forgery Detection Approach Based on Edge Detection

Recently, digital images have become the most used data, thanks tohigh internet speed and high resolution, cheap and easily accessible digitalcameras. We generate, transmit and store millions of images every second.Most of these images are insignificant images containing only personal information.However, in many fields such as banking, finance, public institutions,and educational institutions, the images of many valuable objects like IDcards, photographs, credit cards, and transaction receipts are stored andtransmitted to the digital environment. These images are very significantand must be secured. A valuable image can be maliciously modified by anattacker. The modification of an image is sometimes imperceptible even by theperson who stored the image. In this paper, an active image forgery detectionmethod that encodes and decodes image edge information is proposed. Theproposed method is implemented by designing an interface and applied on atest image which is frequently used in the literature. Various tampering attacksare simulated to test the fidelity of the method. The method not only notifieswhether the image is forged or not but also marks the tampered region ofthe image. Also, the proposed method successfully detected tampered regionsafter geometric attacks, even on self-copy attacks. Also, it didn’t fail on JPEGcompression.

Deep Learning Based Image Forgery Detection Methods

Increasingly advanced image processing technology has made digital image editing easier and easier.With image processing software at one’s fingertips,one can easily alter the content of an image,and the altered image is so realistic that it is illegible to the naked eye.These tampered images have posed a serious threat to personal privacy,social order,and national security.Therefore,detecting and locating tampered areas in images has important practical significance,and has become an important research topic in the field of multimedia information security.In recent years,deep learning technology has been widely used in image tampering localization,and the achieved performance has significantly surpassed traditional tampering forensics methods.This paper mainly sorts out the relevant knowledge and latest methods in the field of image tampering detection based on deep learning.According to the two types of tampering detection based on deep learning,the detection tasks of the method are detailed separately,and the problems and future prospects in this field are discussed.It is quite different from the existing work:(1)This paper mainly focuses on the problem of image tampering detection,so it does not elaborate on various forensic methods.(2)This paper focuses on the detectionmethod of image tampering based on deep learning.(3)This paper is driven by the needs of tampering targets,so it pays more attention to sorting out methods for different tampering detection tasks.