69 resources related to Image Forensics
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No organizations are currently tagged "Image Forensics"
The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE
The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.
Multimedia technologies, systems and applications for both research and development of communications, circuits and systems, computer, and signal processing communities.
The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.
Multimedia Content Understanding, Modeling, Management, andRetrieval• Multimedia meta-modeling techniques• Multimedia storage systems, databases, and retrieval• Multimedia data segmentation• Image, audio, video, genre clustering & classification• Video summarization and story generation• Speaker identification, recognition, and location• Object, event, emotion, text detection and recognition• Mosaic, video panorama and background generation• Multimedia semantics, ontologies, annotation, concept detection andlearning• Personalization and user preferences• 3D and depth information• Viewer perception, emotion analysis and visual attention• Multimedia datasets and open source code for research• Multimedia recommender systems• Fake multimedia detectionMultimedia Interfaces• Multimedia information visualization and interactive systems• Multimodal user interfaces: design, engineering, modality-abstractions,etc.• Multimedia tools for authoring, analyzing, editing
No periodicals are currently tagged "Image Forensics"
Proceedings 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC), 2013
Fake images have become ubiquitous today in society. Identifying the authenticity and integrity of digital images becomes increasingly important in digital forensics cases. This paper presents a review of some significant work in the field of image forensic and builds an image forensic system to verify the forged evidences by analyzing the EXIF information and statistical features. Some synthetic examples ...
2017 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW), 2017
This paper presents a new method on image forensics application using the Error Diffusion Block Truncation Coding (EDBTC) feature. The image forensics tries to detect the copy-move forgery image regions on the forged image. Firstly, an image is divided into several non-overlapping image blocks. The image feature is further derived for each image block. Herein, two image features, namely Color ...
2015 5th IEEE International Conference on System Engineering and Technology (ICSET), 2015
The advancement in digital image tampering has encouraged studies in the image forensics fields. The image tampering can be found over various image formats such as Joint Photographic Experts Group (JPEG). JPEG is the most common format that supported by devices and applications. Therefore, researchers have been studying the implementation of JPEG algorithm in the image forensics. In this paper, ...
Machine Learning in Image Steganalysis, None
This chapter contains sections titled: Image Forensics Conclusions and Notes
China Communications, 2016
Copy-Move Forgery (CMF) is one of the simple and effective operations to create forged digital images. Recently, techniques based on Scale Invariant Features Transform (SIFT) are widely used to detect CMF. Various approaches under the SIFT-based framework are the most acceptable ways to CMF detection due to their robust performance. However, for some CMF images, these approaches cannot produce satisfactory ...
Seeing the Invisibles: A Backstage Tour of Information Forensics
P2020 Establishing Image Quality Standards for Automotive
Hamid R Tizhoosh - Fuzzy Image Processing
Solving Sparse Representation for Image Classification using Quantum D-Wave 2X Machine - IEEE Rebooting Computing 2017
Zohara Cohen AMA EMBS Individualized Health
George Oikonomou’s Paper: Traffic Forensics for IPv6-Based Wireless Sensor Networks and the IoT: WF-IoT 2016
Broadband IQ, Image Reject, and Single Sideband Mixers: MicroApps 2015 - Marki Microwave
IEEE Low-Power Image Recognition Challenge (LPIRC)
Tapping the Computing Power of the Unconscious Brain
Welcome: Low Power Image Recognition Challenge
CPIQ Update and the Case for Image Quality Standards in Automotive
Q&A with Ryan Dailey: IEEE Rebooting Computing Podcast, Episode 12
Low Power Image Recognition: The Challenge Continues
Robotics History: Narratives and Networks Oral Histories: Ray Jarvis
My Computer Speaks Colors! Fuzzy Color Spaces for Image Understanding, Description and Retrieval
Robotics History: Narratives and Networks Oral Histories: Minoru Asada
Resistive Coupled VO2 Oscillators for Image Recognition - Elisabetta Corti - ICRC 2018
Tech News on IEEE.tv
Noise Enhanced Information Systems: Denoising Noisy Signals with Noise
Fake images have become ubiquitous today in society. Identifying the authenticity and integrity of digital images becomes increasingly important in digital forensics cases. This paper presents a review of some significant work in the field of image forensic and builds an image forensic system to verify the forged evidences by analyzing the EXIF information and statistical features. Some synthetic examples and perceptually credible forgeries are tested in this paper. Experimental results show the effectiveness of these techniques in our system.
This paper presents a new method on image forensics application using the Error Diffusion Block Truncation Coding (EDBTC) feature. The image forensics tries to detect the copy-move forgery image regions on the forged image. Firstly, an image is divided into several non-overlapping image blocks. The image feature is further derived for each image block. Herein, two image features, namely Color Feature (CF) and Bit Feature (BF), are composed from the EDBTC compressed data stream. The forged region is detected while the image feature of this region is similar to the image feature of other region separated far away. As documented in the experimental section, the proposed method gives a promising result on the image forensics taks, and, at the same time, outperforms the former existing scheme.
The advancement in digital image tampering has encouraged studies in the image forensics fields. The image tampering can be found over various image formats such as Joint Photographic Experts Group (JPEG). JPEG is the most common format that supported by devices and applications. Therefore, researchers have been studying the implementation of JPEG algorithm in the image forensics. In this paper, the Error Level Analysis (ELA) technique was evaluated with different types of image tampering, including JPEG compression, image splicing, copy-move and image retouching. From the experiment, the ELA showed reliability with JPEG compression, image splicing and image retouching forgery.
This chapter contains sections titled: Image Forensics Conclusions and Notes
Copy-Move Forgery (CMF) is one of the simple and effective operations to create forged digital images. Recently, techniques based on Scale Invariant Features Transform (SIFT) are widely used to detect CMF. Various approaches under the SIFT-based framework are the most acceptable ways to CMF detection due to their robust performance. However, for some CMF images, these approaches cannot produce satisfactory detection results. For instance, the number of the matched key-points may be too less to prove an image to be a CMF image or to generate an accurate result. Sometimes these approaches may even produce error results. According to our observations, one of the reasons is that detection results produced by the SIFT-based framework depend highly on parameters whose values are often determined with experiences. These values are only applicable to a few images, which limits their application. To solve the problem, a novel approach named as CMF Detection with Particle Swarm Optimization (CMFD-PSO) is proposed in this paper. CMFD-PSO integrates the Particle Swarm Optimization (PSO) algorithm into the SIFT-based framework. It utilizes the PSO algorithm to generate customized parameter values for images, which are used for CMF detection under the SIFT-based framework. Experimental results show that CMFD-PSO has good performance.
Using images in case investigation may doubt that the image are forged or not. Image forensics is an approach for image validity. In this paper, we propose a new method for detecting forgery of image that use of the truth from XOR comparison between two images, and the determinant of 3x3 pixels for more performance instead of comparing pixel by pixel. Euclidean of a pixel is computed to reduce the dimensions from RGB vectors. In this experiment, XOR with pixel-by-pixel and XOR with determinant of 3x3 pixels were compared. Fifty images were used: 25 real images and 25 forged images. The result showed that our new method increased speed 36.42% and the error was at 13%.
Combining with newly emerging and mature practical tools in digital image forensics, we explore a typical inspection workflow for digital image forensic authentication in practical case examination. The techniques both in digital and multimedia forensics are integrated into the workflow which covers the whole life cycle inspection of digital images. We divide the workflow into the digital-based, metadata-based, and statistical-based image forensics techniques. In the digital-based image forensics, the operation system (OS) and the physical storage examination are considered. In the metadata-based image authentication, we inspect the coding related metadata. The statistical related artifact detection, such as the sensor noise, recompression, copy- pasting, resampling, etc., is included in the statistical-based image forensics. The proposed inspection workflow for digital image forensic authentication can well deal with the anti and counter forensics techniques in the image tampering.
In image forensics, JPEG compression history estimation problem consists of three stages: JPEG compression detection, color model identification and subsampling, and estimation of quantization steps. Although, it is well known the importance of the phase spectrum information of digital signals and its application in image processing, the phase spectrum has been poorly explored for image forensics. In this paper, a novel method for detecting JPEG compression using statistical moments of the phase spectrum is presented. The experimentation results show that the detection rates achieved by the proposed strategy are competitive with other methods reported in the state-of-the-art.
In Image Forensics, very often, copy-move attack is countered by resorting at instruments based on matching local features descriptors, usually SIFT. On the other side, to overcome such techniques, smart hackers can try firstly to remove keypoints before performing image patch cloning in order to inhibit the successive matching operation. However, keypoint removal determines per se some suspicious empty areas that could indicate that a manipulation has occurred. In this paper, the goal to nullify SIFT matches while preserving keypoints is pursued. The basic idea is to succeed in altering the features descriptor by means of shifting the dominant orientation associated to a specific keypoint. In fact, to provide rotation invariance, all the values of the descriptor are computed according to such orientation. So doing, it should impair the whole matching phase.
High dynamic range (HDR) imaging is attracting an increasing deal of attention in the multimedia community, yet its forensic problems have been little studied so far. This paper proposes an HDR image forensic method, which aims at differentiating HDR images created from multiple low dynamic range (LDR) images from those created from a single LDR image by inverse tone mapping. For each kind of HDR image, a Gaussian mixture model is learned. Thereafter, an HDR image forensic feature is constructed based on calculating the Fisher scores. With comparison to a steganalytic feature and a texture/facial analysis feature, experimental results demonstrate the efficiency of the proposed method in HDR image forensic classification on whole images as well as small blocks, for three inverse tone mapping methods.
No standards are currently tagged "Image Forensics"