Illuminant-Based Transformed Spaces for Image Forensics

Carvalho, Tiago; Faria, Fábio A.; Pedrini, Hélio; Torres, Ricardo da Silva; Rocha, Anderson

doi:10.1109/tifs.2015.2506548

Cited by 90 publications

(25 citation statements)

References 30 publications

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“…Splicing detection [20] DCT coefficient distributions of each block Not applicable [21] Multiscale scheme based on Benford's law Not applicable [22] CFA artifacts Possible but with low robustness [23] PRNU noises Possible but with low robustness [24] Multiscale scheme based on PRNU noises Possible but with low robustness [25] Blur type inconsistency Not applicable [26] Illuminant-based transform spaces Not applicable [27] Two-view geometrical constraints Not applicable [28] Planar homographies Not applicable Image retouching detection [29] Block similarities and distances Not applicable [3] Peak/gap artifacts Not applicable Scribble-based method [30] Neighboring pixels with similar intensities should have similar colors User scribbles [31] Construct color and texture dictionaries User scribbles…”

Section: Methodsmentioning

confidence: 99%

Fake Colorized Image Detection

Guo

Cao

Zhang

et al. 2018

IEEE Trans.Inform.Forensic Secur.

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Abstract-Image forensics aims to detect the manipulation of digital images. Currently, splicing detection, copy-move detection and image retouching detection are drawing much attentions from researchers. However, image editing techniques develop with time goes by. One emerging image editing technique is colorization, which can colorize grayscale images with realistic colors. Unfortunately, this technique may also be intentionally applied to certain images to confound object recognition algorithms. To the best of our knowledge, no forensic technique has yet been invented to identify whether an image is colorized. We observed that, compared to natural images, colorized images, which are generated by three state-of-the-art methods, possess statistical differences for the hue and saturation channels. Besides, we also observe statistical inconsistencies in the dark and bright channels, because the colorization process will inevitably affect the dark and bright channel values. Based on our observations, i.e., potential traces in the hue, saturation, dark and bright channels, we propose two simple yet effective detection methods for fake colorized images: Histogram based Fake Colorized Image Detection (FCID-HIST) and Feature Encoding based Fake Colorized Image Detection (FCID-FE). Experimental results demonstrate that both proposed methods exhibit a decent performance against multiple state-of-the-art colorization approaches.

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Section: Methodsmentioning

confidence: 99%

Fake Colorized Image Detection

Guo

Cao

Zhang

et al. 2018

IEEE Trans.Inform.Forensic Secur.

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“…Initially, a suitable CNN structure must be figured out, and the selection of each layer, such as the convolutional layers and the max-pooling layers, and the dropout depends strongly on the experiments. By reviewing previous research and the dataset size [1,4], the model is configured to accept 256 × 256 images as the input. The TFID model contains six convolution layers that are in charge of extracting abstract features.…”

Section: Methodsmentioning

confidence: 99%

“…Image tampering is an effective technique that can be exploited to manipulate images. There are three standard techniques in image tampering, including copy-move, image splicing, and image retouching [4]. The copy-move method refers to the process of copying some parts from a source image and putting them into a target image, whereas the image splicing technique combines two or more images to create a composite image.…”

Section: Introductionmentioning

confidence: 99%

Tampered and Computer-Generated Face Images Identification Based on Deep Learning

et al. 2020

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Image forgery is an active topic in digital image tampering that is performed by moving a region from one image into another image, combining two images to form one image, or retouching an image. Moreover, recent developments of generative adversarial networks (GANs) that are used to generate human facial images have made it more challenging for even humans to detect the tampered one. The spread of those images on the internet can cause severe ethical, moral, and legal issues if the manipulated images are misused. As a result, much research has been conducted to detect facial image manipulation based on applying machine learning algorithms on tampered face datasets in the last few years. This paper introduces a deep learning-based framework that can identify manipulated facial images and GAN-generated images. It is comprised of multiple convolutional layers, which can efficiently extract features using multi-level abstraction from tampered regions. In addition, a data-based approach, cost-sensitive learning-based approach (class weight), and ensemble-based approach (eXtreme Gradient Boosting) is applied to the proposed model to deal with the imbalanced data problem (IDP). The superiority of the proposed model that deals with an IDP is verified using a tampered face dataset and a GAN-generated face dataset under various scenarios. Experimental results proved that the proposed framework outperformed existing expert systems, which has been used for identifying manipulated facial images and GAN-generated images in terms of computational complexity, area under the curve (AUC), and robustness. As a result, the proposed framework inspires the development of research on image forgery identification and enables the potential to integrate these models into practical applications, which require tampered facial image detection.

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“…Silva et al (2015) proposed a method tailored for copy-move image forgery detection based on a multi-scale analysis of the input image. The input Figure 4 -Overview of the method proposed by Carvalho et al (2016). An image is analyzed by segmenting all suspected faces.…”

Section: (B)mentioning

confidence: 99%

Counteracting the contemporaneous proliferation of digital forgeries and fake news

Ferreira

Carvalho

Andaló

et al. 2019

An. Acad. Bras. Ciênc.

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Fake news has been certainly the expression of the moment: from political round table discussions to newspapers to social and mainstream media. It is everywhere. With such an intense discussion and yet few effective ways to combat it, what can be done? Providing methods to fight back even the least harming hoax is a social responsibility. To look for authenticity in a wide sea of fake news, every detail is a lead. Image appearance and semantic content of text and images are some of the main properties, which can be analyzed to reveal even the slightest lie. In this vein, this work overviews some recent methods applicable to the verification of dubious content in text and images, and discusses how we can put them together as an option to curb away the proliferation of unverified and phony "facts". We briefly present the main idea behind each method, highlighting real situations where they can be applied and discussing expected results. Ultimately, we show how new research areas are working to seamlessly stitch together all these methods so as to provide a unified analysis and to establish the synchronization in space and timethe X-Coherence-of heterogeneous sources of information documenting real-world events.

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Illuminant-Based Transformed Spaces for Image Forensics

Cited by 90 publications

References 30 publications

Fake Colorized Image Detection

Fake Colorized Image Detection

Tampered and Computer-Generated Face Images Identification Based on Deep Learning

Counteracting the contemporaneous proliferation of digital forgeries and fake news

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