General-purpose image forensics using patch likelihood under image statistical models

Wang, Fan; Wang, Kai; Cayre, François

doi:10.1109/wifs.2015.7368606

Cited by 50 publications

(52 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Image forensics. Various problems have been considered by image forensics researchers [12], such as camera identification [2], identification of synthetic images [4,14], detection of falsification such as splicing and copy-move [3,17], and detection of image manipulation [13,5,10,1]. We are interested in the last topic of detecting image manipulations.…”

Section: Background and Research Problemmentioning

confidence: 99%

Weakly Supervised Adaptation to Re-sizing for Image Manipulation Detection on Small Patches

Darmet

Wang

Cayre

2020

Digital Forensics and Watermarking

Self Cite

View full text Add to dashboard Cite

Basic image processing operations like median filtering and Gaussian blurring in general do not change the semantic content of an image, although they are commonly used to cover fingerprints of falsification that does alter image content such as copy-move and splicing. Therefore image forensics researchers are interested in detecting these basic operations. Some existing detectors track local inconsistencies in statistics of the image. However these statistics are very sensitive to image development process. Thus pre-processing operations can be damaging for performances of such detectors. In this paper, we focus on a very common pre-processing operation, i.e., re-sizing, and study how it affects performance when trying to detect several image processing operations on small patches, with Gaussian Mixture Model (GMM) as feature extractor and a Dense Neural Network (DNN) as classifier. We first show performance drops. We then introduce an adaptation method which relies on better fit to testing data for the feature extraction and fine-tuning for the neural network classifier. Experimental results show that our method is able to improve results with very few labeled testing samples. We also present comparisons with an improved version of a recent CNN(Convolutional Neural Network)-based method.

show abstract

Section: Background and Research Problemmentioning

confidence: 99%

Weakly Supervised Adaptation to Re-sizing for Image Manipulation Detection on Small Patches

Darmet

Wang

Cayre

2020

Digital Forensics and Watermarking

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, some methods rely on machine learning [12,16,18] and have reported good performance. However, these methods essentially depend on the availability and quality of training data, which is not always guaranteed.…”

Section: Related Workmentioning

confidence: 99%

“…The illumination environment in pictures also presents some consistency: directions of lights [21], shadows [30] and illumination colors [6] can be estimated and used as cues. However, the methods mentioned above are intrinsically sensitive only to specific manipulations.In addition, some methods rely on machine learning [12,16,18] and have reported good performance. However, these methods essentially depend on the availability and quality of training data, which is not always guaranteed.An interesting approach for forgery detection relies on the characteristics of the digital camera, such as the color filter array (CFA) interpolation artifacts [17], lens aberration [22] and sensor pattern noise (SPN) [32], which has drawn considerable attention due to the uniqueness of individual cameras and the stability against environmental conditions.…”

mentioning

confidence: 99%

Multi-scale segmentation strategies in PRNU-based image tampering localization

Zhang

Tang

Yang

et al. 2019

Multimed Tools Appl

View full text Add to dashboard Cite

With the rapid development of advanced media technology, especially the popularization of digital cameras and image editing software, digital images can be easily forged without leaving visible clues. Therefore, image forensics technology for identifying the accuracy, integrity, and originality of digital images has become increasingly important. Photoresponse non-uniformity (PRNU) noise, a unique fingerprint of imaging sensors, is a valuable forgery detection tool because of its consistently good detection performance. All kinds of forgeries, including copy-move and splicing, can be dealt with in a uniform manner. This paper addresses the problem of forgery localization based on PRNU estimation and aims to improve the resolution of PRNU-based algorithms. Different from traditional overlapping and sliding window-based methods, in which PRNU correlations are estimated on overlapped patches, the proposed scheme is analyzed based on nonoverlapping and irregular patches. First, the test image is segmented into nonoverlapped patches with multiple scales. Second, correlations of PRNU are estimated on nonoverlapped patches to obtain the real-valued candidate tampering probability map for each individual scale. Then, all of the candidate maps are fused into a single and more reliable probability map through an adaptive window strategy. In the final step, the final decision map is obtained by adopting a conditional random field (CRF) to model neighborhood interactions. The contributions of this work include the following: a novel PRNUbased forgery localization scheme using multi-scale nonoverlapping segmentation is proposed for the first time. Furthermore, the adaptive fusion strategy involves selecting the best candidate tampering probability individually for each location in the image. Additionally, the experimental results prove that the proposed scheme can achieve much better detection results and robustness compared with the existing state-of-the-art PRNUbased methods.Today's advanced media technology, such as digital image processing, video coding [38], highefficiency video coding (HEVC) [24,35], Internet of Things (IoT) [36,45], and cloud computing (CC) [39], represents a fascinating time that will considerably affect daily life. In particular, digital images are being used in many applications such as the military, medical diagnosis, art pieces, and photography. The reliability of digital images is thus becoming an important issue. However, currently, it is very easy to manipulate digital images without leaving visible traces using photo editing software. Therefore, it is important to focus on the image forensics field. One of the principal problems in image forensics is determining whether a particular image is authentic and, if manipulated, to localize which parts have been altered. Since forgery localization requires pixel-level analysis rather than image-level analysis, it faces more challenges compared to forgery detection. Related worksInstead of using digital watermarks [3] and signatures [47], many passive...

show abstract

“…For instance, by modeling image processing operations as steganography, Qiu et al [18] proposed to use steganalytic features, such as SPAM [19], SRM [20] and LBP [21], to identify six typical image processing operations. Fan et al [22] adopted Gaussian mixture models to model the statistics of images processed by different image operations. Recently, Li et al [23] proposed a compact universal feature set from SRM [20] to identify 11 typical image processing operations.…”

Section: Introductionmentioning

confidence: 99%

Image processing operations identification via convolutional neural network

Chen

Luo

et al. 2020

Sci. China Inf. Sci.

View full text Add to dashboard Cite

In recent years, image forensics has attracted more and more attention, and many forensic methods have been proposed for identifying image processing operations. Up to now, most existing methods are based on hand crafted features, and just one specific operation is considered in their methods. In many forensic scenarios, however, multiple classification for various image processing operations is more practical. Besides, it is difficult to obtain effective features by hand for some image processing operations. In this paper, therefore, we propose a new convolutional neural network (CNN) based method to adaptively learn discriminative features for identifying typical image processing operations. We carefully design the high pass filter bank to get the image residuals of the input image, the channel expansion layer to mix up the resulting residuals, the pooling layers, and the activation functions employed in our method. The extensive results show that the proposed method can outperform the currently best method based on hand crafted features and three related methods based on CNN for image steganalysis and/or forensics, achieving the state-of-the-art results. Furthermore, we provide more supplementary results to show the rationality and robustness of the proposed model.

show abstract

General-purpose image forensics using patch likelihood under image statistical models

Cited by 50 publications

References 18 publications

Weakly Supervised Adaptation to Re-sizing for Image Manipulation Detection on Small Patches

Weakly Supervised Adaptation to Re-sizing for Image Manipulation Detection on Small Patches

Multi-scale segmentation strategies in PRNU-based image tampering localization

Image processing operations identification via convolutional neural network

Contact Info

Product

Resources

About