Showing papers on "Image file formats published in 2018"

Cnn-Based Detection of Generic Contrast Adjustment with Jpeg Post-Processing

Abstract: Detection of contrast adjustments in the presence of JPEG post processing is known to be a challenging task. JPEG post processing is often applied innocently, as JPEG is the most common image format, or it may correspond to a laundering attack, when it is purposely applied to erase the traces of manipulation. In this paper, we propose a CNN-based detector for generic contrast adjustment, which is robust to JPEG compression. The proposed system relies on a patch-based Convolutional Neural Network (CNN), trained to distinguish pristine images from contrast adjusted images, for some selected adjustment operators of different nature. Robustness to JPEG compression is achieved by training the CNN with JPEG examples, compressed over a range of Quality Factors (QFs). Experimental results show that the detector works very well and scales well with respect to the adjustment type, yielding very good performance under a large variety of unseen tonal adjustments.

Analytic image format for visual computing

Abstract: In one embodiment, an apparatus comprises a storage device and a processor. The storage device stores a plurality of images captured by a camera. The processor: accesses visual data associated with an image captured by the camera; determines a tile size parameter for partitioning the visual data into a plurality of tiles; partitions the visual data into the plurality of tiles based on the tile size parameter, wherein the plurality of tiles corresponds to a plurality of regions within the image; compresses the plurality of tiles into a plurality of compressed tiles, wherein each tile is compressed independently; generates a tile-based representation of the image, wherein the tile-based representation comprises an array of the plurality of compressed tiles; and stores the tile-based representation of the image on the storage device.

A New Method of Image Steganography Using 7th Bit of a Pixel as Indicator by Introducing the Successive Temporary Pixel in the Gray Scale Image

Abstract: As the internet has become the medium for transferring the sensitive information, the security of the transferred message has become the utmost priority. Image steganography has emerged out as the eminent tool of information hiding that ensures the security of the transmitted data. Image files provide high capacity, and their frequency of availability over the internet is also high. In this paper, a method of image coding is proposed that hides the information along a selected pixel and on the next value of the selected pixel, that is, pixel + 1. One bit is hidden at the selected pixel, and the second bit is hidden on the pixel +1 value. On the basis of the 7th bit of the pixels of an image, a mathematical function is applied at the 7th bit of the pixels, which generates a temporary variable (pixel + 1). The 7th bit of the selected pixel and 7th bit of pixel + 1 are used for information hiding and extraction. On the basis of a combination of these two values, two bits of the message can be hidden on each pixel. After implementation, the efficiency of the method is checked on the basis of parameters like PSNR and MSE, and then comparison with some already proposed techniques was done. This proposed image steganography showed interesting, promising results when compared with other existing techniques.

Transforming Dermatologic Imaging for the Digital Era: Metadata and Standards

Abstract: Imaging is increasingly being used in dermatology for documentation, diagnosis, and management of cutaneous disease. The lack of standards for dermatologic imaging is an impediment to clinical uptake. Standardization can occur in image acquisition, terminology, interoperability, and metadata. This paper presents the International Skin Imaging Collaboration position on standardization of metadata for dermatologic imaging. Metadata is essential to ensure that dermatologic images are properly managed and interpreted. There are two standards-based approaches to recording and storing metadata in dermatologic imaging. The first uses standard consumer image file formats, and the second is the file format and metadata model developed for the Digital Imaging and Communication in Medicine (DICOM) standard. DICOM would appear to provide an advantage over using consumer image file formats for metadata as it includes all the patient, study, and technical metadata necessary to use images clinically. Whereas, consumer image file formats only include technical metadata and need to be used in conjunction with another actor-for example, an electronic medical record-to supply the patient and study metadata. The use of DICOM may have some ancillary benefits in dermatologic imaging including leveraging DICOM network and workflow services, interoperability of images and metadata, leveraging existing enterprise imaging infrastructure, greater patient safety, and better compliance to legislative requirements for image retention.

Systems and methods for decoding image files containing depth maps stored as metadata

Abstract: Systems and methods in accordance with embodiments of the invention are configured to render images using light field image files containing an image synthesized from light field image data and metadata describing the image that includes a depth map. One embodiment of the invention includes a processor and memory containing a rendering application and a light field image file including an encoded image, a set of low resolution images, and metadata describing the encoded image, where the metadata comprises a depth map that specifies depths from the reference viewpoint for pixels in the encoded image. In addition, the rendering application configures the processor to: locate the encoded image within the light field image file; decode the encoded image; locate the metadata within the light field image file; and post process the decoded image by modifying the pixels based on the depths indicated within the depth map and the set of low resolution images to create a rendered image.

Performance evaluation measurement of image steganography techniques with analysis of LSB based on variation image formats

Abstract: Recently, Steganography is an outstanding research area which used for data protection from unauthorized access. Steganography is defined as the art and science of covert information in plain sight in various media sources such as text, images, audio, video, network channel etc. so, as to not stimulate any suspicion; while steganalysis is the science of attacking the steganographic system to reveal the secret message. This research clarifies the diverse showing the evaluation factors based on image steganographic algorithms. The effectiveness of a steganographic is rated to three main parameters, payload capacity, image quality measure and security measure. This study is focused on image steganographic which is most popular in in steganographic branches. Generally, the Least significant bit is major efficient approach utilized to embed the secret message. In addition, this paper has more detail knowledge based on Least significant bit LSB within various Images formats. All metrics are illustrated in this study with arithmetical equations while some important trends are discussed also at the end of the paper.

CNN-Based Detection of Generic Constrast Adjustment with JPEG Post-processing

Abstract: Detection of contrast adjustments in the presence of JPEG postprocessing is known to be a challenging task. JPEG post processing is often applied innocently, as JPEG is the most common image format, or it may correspond to a laundering attack, when it is purposely applied to erase the traces of manipulation. In this paper, we propose a CNN-based detector for generic contrast adjustment, which is robust to JPEG compression. The proposed system relies on a patch-based Convolutional Neural Network (CNN), trained to distinguish pristine images from contrast adjusted images, for some selected adjustment operators of different nature. Robustness to JPEG compression is achieved by training the CNN with JPEG examples, compressed over a range of Quality Factors (QFs). Experimental results show that the detector works very well and scales well with respect to the adjustment type, yielding very good performance under a large variety of unseen tonal adjustments.

The Performance of Cover Image Steganography for Hidden Information within Image File using Least Significant bit algorithm

Abstract: The Protection of information with steganography is the art and science of writing hidden is a hidden data method in various data type to conceal data and unauthorized person or attacker does not know these messages are camouflage. Moreover, we can send text or image was concealed by cover image and adjust some bits of the original to the receiver. The text or image file concealed within cover image can secure the original data from the attacker. In this paper, we studied the performance of the optimal file size to camouflage and the hidden data method with cover image using Least Significant bit (LSB). The dataset are text file (.txt), document File (.doc) and image file which have dimension as follow; 800x600, 1200x900, 1600x1200, 2000x1500, 2400x1800, 2800x2100, 3200x2400, 3600x2700 and 4000x3000 The image file sizes are 576KB, 1.14MB, 1.85MB, 2.66MB, 3.58MB, 4.57MB, 5.54MB, 6.46MB and 7.31MB respectively, that used as cover image which was concealed and decrypted from Stego image to plaintext. The result showed that the cover image size is 576 KB is the optimal file for hidden data 175 KB or 30% of the cover image. The cover image size is 4.57 MB is the optimal file for hidden data 2.10 MB or 45.95% of the cover image. And the cover image size is 7.31 MB which dimension is 4000*3000 can camouflage file up to 4.29 MB or 58.69% of the cover image. Therefore, the larger cover image can hidden larger text file up to size of cover image vary to the size of cover image that used for hidden data.

Simulated Annealing for JPEG Quantization

Abstract: JPEG is one of the most widely used image formats, but in some ways remains surprisingly unoptimized, perhaps because some natural optimizations would go outside the standard that defines JPEG. We show how to improve JPEG compression in a standard-compliant, backward-compatible manner, by finding improved default quantization tables. We describe a simulated annealing technique that has allowed us to find several quantization tables that perform better than the industry standard, in terms of both compressed size and image fidelity. Specifically, we derive tables that reduce the FSIM error by over 10% while improving compression by over 20% at quality level 95 in our tests; we also provide similar results for other quality levels. While we acknowledge our approach can in some images lead to visible artifacts under large magnification, we believe use of these quantization tables, or additional tables that could be found using our methodology, would significantly reduce JPEG file sizes with improved overall image quality.

Identifying Image Provenance: An Analysis of Mobile Instant Messaging Apps

Abstract: Studying the impact of sharing platforms like social networks and messaging services on multimedia content nowadays represents a due step in multimedia forensics research. In this framework, we study the characteristics of images that are uploaded and shared through three popular mobile messaging apps combined with two different sending mobile operating systems (OS). In our analysis, we consider information contained both in the image signal and in the metadata of the image file. We show that it is generally possible to identify a posteriori the last app and the OS that have been used for uploading. This is done by considering different scenarios involving images shared both once and twice. Moreover, we show that, by leveraging the knowledge of the last sharing app and system, it is possible to retrieve information on the previous sharing step for double shared images. In relation to prior works, a discussion on the influence of the rescaling and recompression mechanism - usually performed differently through apps and OSs - is also proposed, and the feasibility of retrieving the compression parameters of the image before being shared is assessed.

The Method of Encoding Information in the Images Using Numerical Line Bundles

Abstract: The possibility of encoding information by the method of the least significant bit in the image using the model of a numerical ruler-bundle is presented. When using graphic formats, it becomes possible to hide not only text messages, but also other images and files. For these purposes, technology based on the numerical ruler-bundle is used, which reduces to the replacement of certain pixels in the image, which allows creating efficient algorithms for encoding and decoding.

Multi-pass compression of uncompressed data

Abstract: Introduced here is a technique to create small compressed image files while preserving data quality upon decompression. Upon receiving an uncompressed data, such as an image, a video, an audio, and/or a structured data, a machine learning model identifies an object in the uncompressed data such as a house, a dog, a text, a distinct audio signal, a unique data pattern, etc. The identified object is compressed using a compression treatment optimized for the identified object. The identified object, either before or after the compression, is removed from the uncompressed data. The uncompressed data with the identified object removed is compressed using a standard compression treatment.

An Improved DCT Based Steganography Technique

Abstract: In this paper, a steganographic technique for hiding secret data in image file formats is proposed. This technique uses the Quantized DCT coefficients for hiding the secret information. The proposed steganographic method can provide a high information hiding capacity and successfully increase the security.

LSB Based Text and Image Steganography Using AES Algorithm

Abstract: The steganography is the art of hiding data in another data. Secret information like messages, images, audio and video can be hidden inside the cover image. The main objective is to hide the secret message or image inside the image using Least Significant Bit (LSB) technique. To protect and provide security for the hidden message or image, Advanced Encryption Standard (AES) Algorithm is used. Various image formats with different text length or image size are compared. Efficiency of algorithm is estimated by Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) where higher PSNR value gives the high-quality image. Steganography plays an important role in applications like medical, military, OTP (One Time Password), copyright etc.

Performance analysis of compression algorithms for information security: A Review

Abstract: Data compression is a vital part of information security, since compressed data is much more secure and convenient to handle. Effective data compression technique creates an effective, secure, easy communicable & redundant data. There are two types of compression algorithmic techniques: - lossy and lossless. These techniques can be applied to any data format like text, audio, video or image file. The primary objective of this study was to analyse data compression techniques used for information security techniques like steganography, cryptography etc. Four each, lossy and lossless techniques are implemented and evaluated on parameters like- file size, saving percentage, time, compression ratio and speed. Detailed analysis shows that the lossy techniques performs better quantitatively whereas lossless is better qualitatively. However, lossless techniques are more effective as there is no data loss during the process. Among all, Huffman encoding outperforms other algorithms.

A Watermark-Based Scheme for Authenticating JPEG Image Integrity

Abstract: JPEG is the most common image format in smartphones, computers, or digital cameras. Because numerous JPEG images are easily shared and distributed, there are privacy and security concerns for these images. Hence, the JPEG committee has started a standardization called JPEG privacy and security to resolve these issues. The forgery detection of a JPEG image is the key objective of JPEG privacy and security. In this paper, we propose a novel forgery detection method that is watermark-based, for authenticating JPEG image integrity in the discrete cosine transform (DCT) domain. The proposed method aims at 100% detection accuracy for typical forgeries on JPEG image DCT blocks and to counter a well-known forgery attack called collage attack. For this purpose, the method splits the host image into a group of blocks (GOB). A watermark is generated by collaborating with the neighboring GOBs and is embedded into the GOBs. The experimental results using various sample images show the superiority of the proposed method, exhibiting a negligible visual difference between the original and watermarked JPEG images.

A Dynamic DNA for Key-based Cryptography

Abstract: A dynamic DNA for key-based Cryptography that encrypt and decrypt plain text characters, text file, image file and audio file using DNA sequences. Cryptography is always taken as the secure way while transforming the confidential information over the network such as LAN, Internet. But over the time, the traditional cryptographic approaches are been replaced with more effective cryptographic systems such as Quantum Cryptography, Biometric Cryptography, Geographical Cryptography and DNA Cryptography. This approach accepts the DNA sequences as the input to generate the key that going to provide two stages of data security.

Detecting Android Malware Using Bytecode Image

Abstract: In recent years, there is a rapid increase in the number of Android based malware. In this paper we propose a malware detection method using bytecode code image. We firstly extract bytecode file from Android APK file, and then convert the bytecode file into an image file. Finally we use convolution neural network (CNN) to classify malware. the proposed method directly convert a bytecode file into an image data, so CNN can automatically learn features of malware, and use the learned features to classify malware. Especially for malware which uses polymorphic techniques to encrypt functional code, the proposed method can detect it without using unpacking tools. The experimental results show it is feasible to detect malware using CNN, especially for detecting encrypted malware.

Container application deployment method and device, computer equipment and storage medium

Abstract: The application relates to a container application deployment method and device, computer equipment and a storage medium. The method includes the following steps: receiving an application deployment request sent by a terminal, wherein the application deployment request includes a code identifier; pulling a corresponding code file in a code repository according to the code identifier; parsing the code file, determining configuration parameters required to deploy a target application corresponding to the application deployment request, and creating a target mirror image according to the configuration parameters; adding the code file to the target mirror image to generate a target mirror image file; and starting the target mirror image file to generate a container application, acquiring an access address of the container application, and returning the access address to the terminal. By adopting the method, the deployment threshold of the container application can be lowered, and the deployment efficiency of the container application can be improved.

Method and device for identifying table content in image file and memory medium

Abstract: The invention relates to a method and device for identifying table content in an image file and a memory medium and belongs to the technical field of image identification. The method comprises the steps of obtaining a to-be-identified target image file; carrying out character identification processing on the target image file, thereby obtaining character information in the target image file; carrying out matching processing on the identified character information and a preset word library, thereby obtaining header characters, wherein the matching degree of the header characters is greater thana first threshold; and determining the table content contained in the target image file according to the character information corresponding to the header characters. A table contained in an image can be rapidly and accurately identified, the identification accuracy is improved, the identification operation time also can be reduced, and the use experience of a user is effectively improved.

How FAIR can you get? Image Retrieval as a Use Case to calculate FAIR Metrics

Abstract: A large number of services for research data management strive to adhere to the FAIR guiding principles for scientific data management and stewardship. To evaluate these services and to indicate possible improvements, use-case-centric metrics are needed as an addendum to existing metric frameworks. The retrieval of spatially and temporally annotated images can exemplify such a use case. The prototypical implementation indicates that currently no research data repository achieves the full score. Suggestions on how to increase the score include automatic annotation based on the metadata inside the image file and support for content negotiation to retrieve the images. These and other insights can lead to an improvement of data integration workflows, resulting in a better and more FAIR approach to manage research data.

Automated Extraction of Large Scale Scanned Document Images using Google Vision OCR in Apache Hadoop Environment

Abstract: This Digitalization of documents is now being done in all fields to reduce paper usage. The availability of modern technology in the form of scanners and cameras supports the growth of multimedia data, especially documents stored in the form of image files. Searching a particular text in a large-scale scanned document images is a difficult task if the document is in the form of images where the text has not been extracted. In this research, text extraction method of large-scale scanned document images using Google Vision OCR on the Hadoop architecture is proposed. The object of research is student thesis documents, which includes the cover page, the approval page, and abstract. All documents are stored in the university's digital library. Extraction process begins with preparing the input folder that contains image documents (in JPEG format) in HDFS Apache Hadoop and followed by reading the image document. The image document is then extracted using Google Vision OCR in order to obtain text document (in TXT format) and the result is saved to output folder in Hadoop Distributed File System (HDFS). The same process is repeated for the entire documents in the folder. Test results have shown that the proposed methods was able to extract all test documents successfully. The recognition process achieved 100% accuracy and the extraction time is twice as fast as manual extraction. Google Vision OCR also shows better extraction performance compared to other OCR tools. The proposed automated extraction systems can recognize text in a large-scale image document accurately and can be operated in a real-time environment.

Video behavior quick recognition method for extracting moving target through light stream

Abstract: The invention relates to a video behavior quick recognition method for extracting a moving target through a light stream, and the method comprises the steps: S1, receiving a plurality of videos, reading images in videos frame by frame, and storing the images as a file at an image format; S2, unifying the sizes of all images to a set value, and dividing all videos into a training set and a test set; S3, extracting a light stream image of each video; S4, selecting the light stream image with the largest gray scale value in all videos, extracting a part with the gray scale value being outside a set range, enabling the part to act on an original RGB image of the image, and obtaining a mask image; S5, inputting the mask image and the light stream image, selected by each video in the training set, into a space stream and a time stream of a double-stream convolutional neural network for training; S6, inputting the mask image and the light stream image, selected by each video in the test set,into the space stream and the time stream of a double-stream convolutional neural network for training. Compared with the prior art, the method is high in calculation speed.

Vehicle mounted image projection system

Abstract: A mobile image projection system includes a wireless network interface configured to communicate with at least one remote computing device and an optical projection device communicatively coupled with the wireless network interface and configured to be mounted on a vehicle and project an image on a surface of a vehicle body. The system includes a processor and a memory configured to store at least one image file and instructions that, when executed, cause the processor to receive, using the wireless network interface an image file from the at least one remote computing device and display an image from the received image file on the surface of the vehicle.

The ImageJ Ecosystem: An Open and Extensible Platform for Biomedical Image Analysis

Abstract: ImageJ is an image analysis program that features a recordable macro language, and extensible plug-in architecture. We describe new developments including a new plugin framework that enables more image formats, scripting languages, and enhanced visualization.

A Video Steganography in Spatial, Discrete Wavelet Transform and Integer Wavelet Domain

Abstract: Video steganography is an engineering term define as hiding the secret massage in cover multimedia file like video file, image file. Steganography also identified is the skill and learning of writing word which is want to be hide behind chose one as a cover multimedia file like audio, video or image The main work of in this research increases the capacity in video data hiding within DWT technique. Video are perfect for information hiding because of the amount of area is produced in the storing of videos. The concept elevated the need of video steganography in all field like as e-paying, e-marketing , personal or national security data, and finance as well as the personal communication datas.in this review main on the DWT technique for hiding the data massage. In This research the algorithm for hiding the secret text behind the cover multimedia video file. Also, find out the all operation parameter methods like capacity, bitrate (BR) , (PSNR) Peak signal to noise ratio ,correlation(CR), (MSE) Mean square error, histogram. And in this paper used different-2 cover video file or image file and investigation to best result according to capacity and execution matrix.