Exposing seam carving forgery under recompression attacks by hybrid large feature mining (original) (raw)
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Detection of Seam Carving in Uncompressed Images using eXtreme Gradient Boosting
A digital image forensic approach to detect whether an image has been seam carved or not is investigated herein. Seam carving is a content-aware image retargeting technique which preserves the semantically important content of an image while resizing it. The same technique, however, can be used for malicious tampering of an image. 18 energy, seam, and noise related features defined by Ryu [1] are produced using Sobel’s [2] gradient filter and Rubinstein’s [3] forward energy criterion enhanced with image gradients. An extreme gradient boosting classifier [4] is trained to make the final decision. Experimental results show that the proposed approach improves the detection accuracy from 5 to 10% for seam carved images with different scaling ratios when compared with other state-ofthe- art methods. Index Terms—digital image forensics, seam carving, extreme gradient boosting, content-aware image resizing
Using Patch Analysis Methods to Detect Images Tampered with Seam Insertion
Retargeting images by seam carving and seam insertion is hard to identify; therefore, detection of seam tampered images has been an important but challenging research topic. Aside from existing methods, i.e., those derived from steganography attacks and those based on statistical features, we have proposed a novel detection method in our previous work, referred to as the patch analysis method. This method divides images into 2 × 2 blocks, named as mini-squares, and then searches for one of nine patch types that is likely to recover a mini-square from seam carving. By analyzing the patch transition probability among three-connected mini-squares, we achieved currently best accuracies for detecting seam carved images. Here we extend the application of this method to detect imagestampered with seam insertion. We will present and discuss the experimental results in this paper.
Block-level double JPEG compression detection for image forgery localization
arXiv: Image and Video Processing, 2020
Forged images have a ubiquitous presence in today's world due to ease of availability of image manipulation tools. In this letter, we propose a deep learning-based novel approach which utilizes the inherent relationship between DCT coefficient histograms and corresponding quantization step sizes to distinguish between original and forged regions in a JPEG image, based on the detection of single and double compressed blocks, without fully decompressing the JPEG image. We consider a diverse set of 1,120 quantization matrices collected in a recent study as compared to standard 100 quantization matrices for training, testing, and creating realistic forgeries. In particular, we carefully design the input to DenseNet with a specific combination of quantization step sizes and the respective histograms for a JPEG block. Using this input to learn the compression artifacts produces state-of-the-art results for the detection of single and double compressed blocks of sizes 256times256256 \times 256256times256 ...
ACMTIST, 2014
Steganalysis and forgery detection in image forensics are generally investigated separately. We have designed a method targeting the detection of both steganography and seam-carved forgery in JPEG images. We analyze the neighboring joint density of the DCT coefficients and reveal the difference between the untouched image and the modified version. In realistic detection, the untouched image and themodified version may not be obtained at the same time, and different JPEG images may have different neighboring joint density features. By exploring the self-calibration under different shift recompressions, we propose calibrated neighboring joint density-based approaches with a simple feature set to distinguish steganograms and tampered images from untouched ones. Our study shows that this approach has multiple promising applications in image forensics. Compared to the state-of-the-art steganalysis detectors, our approach delivers better or comparable detection performances with a much smaller feature set while detecting several JPEG-based steganographic systems including DCT-embedding-based adaptive steganography and Yet Another Steganographic Scheme (YASS). Our approach is also effective in detecting seam-carved forgery in JPEG images. By integrating calibrated neighboring density with spatial domain rich models that were originally designed for steganalysis, the hybrid approach obtains the best detection accuracy to discriminate seam-carved forgery from an untouched image. Our study also offers a promising manner to explore steganalysis and forgery detection together.
SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery
ArXiv, 2021
Seam carving is a popular technique for content aware image retargeting. It can be used to deliberately manipulate images, for example, change the GPS locations of a building or insert/remove roads in a satellite image. This paper proposes a novel approach for detecting and localizing seams in such images. While there are methods to detect seam carving based manipulations, this is the first time that robust localization and detection of seam carving forgery is made possible. We also propose a seam localization score (SLS) metric to evaluate the effectiveness of localization. The proposed method is evaluated extensively on a large collection of images from different sources, demonstrating a high level of detection and localization performance across these datasets. The datasets curated during this work will be released to the public.
Enhanced Adaptive Over-Segmentation Technique for Image Forgery Detection under Noise Attacks
International Journal of Computer Science and Mobile Computing (IJCSMC), 2023
The problem of picture fraud has become more pervasive in the current digital era as a direct result of the ease with which complex image manipulation programs can be accessed. This research study provides a comprehensive analysis of several methodologies and approaches that are used to detect and localize cases of picture fraud. The primary objective is to provide a clear understanding of the most cutting-edge methods that are now being utilized, as well as the problems and opportunities that lie ahead. In the field of picture forensics, the focus of this article is on several distinct types of image fraud, such as copy-move, splicing, retouching, and inpainting forgeries. We study the underlying principles that lie behind frequently used detection methods, such as block matching, feature-based analysis, and approaches that are anchored in deep learning. In addition, we go over the benefits and drawbacks of each strategy, focusing on how they apply to a variety of settings. In addition, this article takes a look at the datasets that are typically utilized for the training and evaluation of forgery detection algorithms, highlighting both the benefits and the limitations of those datasets. In addition to this, we examine the numerous assessment metrics that are utilized in order to evaluate the performance of the various methods, with a particular focus on the requirement for standardized benchmark datasets and evaluation methodologies. In addition, we discuss the obstacles that are presented when attempting to identify picture fraud in the real world. These issues include the need to deal with photos that have been compressed, images that have varying resolutions, and the existence of post-processing effects. In this article, we will discuss the significance of multi-modal analysis and the fusion of information obtained from a variety of sources in order to improve the reliability of counterfeit detection systems. The approaches that are used to detect picture counterfeiting are the topic of the next section of this review. We look at several techniques, such as those based on segmentation, texture analysis, and deep neural networks, in order to determine the precise position of forged sections inside a picture. We go through their precision, the amount of computing complexity they have, and the possible uses they have. In conclusion, we discuss the potential of the future. In order to identify and localize forged images, it is necessary to keep up with the latest innovations in image editing software and build forgery detection systems that are more technologically advanced and effective. This is because picture editing techniques are always being refined and improved. We offer prospective research avenues, such as Explainable AI, Generative Adversarial Networks (GANs) for forgeries production, and hybrid techniques to combine the strengths of various detection methods. Specifically, we focus on hybrid approaches to combine the strengths of diverse detection methods. In conclusion, the purpose of this study is to offer academics and industry professionals in This article provides a comprehensive review of the present state of identifying and localizing picture forgeries, which falls under the umbrella of image forensics as a discipline. It encompasses the most recent developments in forgery detection and localisation, making it possible to gain an in-depth comprehension of the subject matter. It is possible to pave the way for more effective and reliable forgery detection systems to protect the integrity of digital pictures in a variety of applications if we grasp the existing approaches as well as the obstacles that they provide.
An Evaluation of Image Forgery Detection Methods using JPEG Compression Properties
Detection of forged images based on JPEG compression properties plays a very crucial role in image forensics. Nowadays, JPEG is the most commonly used compression standard. Most of the digital cameras in the market are mainly exporting JPEG file format. It is very important to identify whether an image has been previously JPEG compressed or not. Recently, few successful approaches have been presented, which, making use of the JPEG compression properties, give us various helpful details of the image under consideration. In this paper, we present an evaluation of image forgery detection methods(IFDM) using JPEG compression properties based on various parameters such as the method employed, the feature extracted, the classifier used, the detection accuracy achieved and the limitations identified. The objective of this paper is to identify the research gaps in IFDM.