JPG Images Research Articles

A UAV Thermal Imaging Format Conversion System and Its Application in Mosaic Surface Microthermal Environment Analysis.

UAV thermal infrared remote sensing technology, with its high flexibility and high temporal and spatial resolution, is crucial for understanding surface microthermal environments. Despite DJI Drones' industry-leading position, the JPG format of their thermal images limits direct image stitching and further analysis, hindering their broad application. To address this, a format conversion system, ThermoSwitcher, was developed for DJI thermal JPG images, and this system was applied to surface microthermal environment analysis, taking two regions with various local zones in Nanjing as the research area. The results showed that ThermoSwitcher can quickly and losslessly convert thermal JPG images to the Geotiff format, which is further convenient for producing image mosaics and for local temperature extraction. The results also indicated significant heterogeneity in the study area's temperature distribution, with high temperatures concentrated on sunlit artificial surfaces, and low temperatures corresponding to building shadows, dense vegetation, and water areas. The temperature distribution and change rates in different local zones were significantly influenced by surface cover type, material thermal properties, vegetation coverage, and building layout. Higher temperature change rates were observed in high-rise building and subway station areas, while lower rates were noted in water and vegetation-covered areas. Additionally, comparing the temperature distribution before and after image stitching revealed that the stitching process affected the temperature uniformity to some extent. The described format conversion system significantly enhances preprocessing efficiency, promoting advancements in drone remote sensing and refined surface microthermal environment research.

Large language model answers medical questions about standard pathology reports.

This study aims to evaluate the feasibility of large language model (LLM) in answering pathology questions based on pathology reports (PRs) of colorectal cancer (CRC). Four common questions (CQs) and corresponding answers about pathology were retrieved from public webpages. These questions were input as prompts for Chat Generative Pretrained Transformer (ChatGPT) (gpt-3.5-turbo). The quality indicators (understanding, scientificity, satisfaction) of all answers were evaluated by gastroenterologists. Standard PRs from 5 CRC patients who received radical surgeries in Shanghai Changzheng Hospital were selected. Six report questions (RQs) and corresponding answers were generated by a gastroenterologist and a pathologist. We developed an interactive PRs interpretation system which allows users to upload standard PRs as JPG images. Then the ChatGPT's responses to the RQs were generated. The quality indicators of all answers were evaluated by gastroenterologists and out-patients. As for CQs, gastroenterologists rated AI answers similarly to non-AI answers in understanding, scientificity, and satisfaction. As for RQ1-3, gastroenterologists and patients rated the AI mean scores higher than non-AI scores among the quality indicators. However, as for RQ4-6, gastroenterologists rated the AI mean scores lower than non-AI scores in understanding and satisfaction. In RQ4, gastroenterologists rated the AI scores lower than non-AI scores in scientificity (P = 0.011); patients rated the AI scores lower than non-AI scores in understanding (P = 0.004) and satisfaction (P = 0.011). In conclusion, LLM could generate credible answers to common pathology questions and conceptual questions on the PRs. It holds great potential in improving doctor-patient communication.

Mobile forensic photogrammetry in the field: Conservative approach to non-collaborative surfaces

Conventional photogrammetry faces challenges with non-textured, transparent, or reflective surfaces, affecting accurate 3D modeling, particularly in forensic documentation. This study evaluates improvements using lower exposure, exposure bracketing, and RAW format for better 3D modeling of non-textured surfaces. Two bodies were photographed under controlled conditions to assess techniques for non-textured surfaces, with a comparison set for textured surfaces. The experiments were conducted in an autopsy room with a Nikon D5500 camera, adjusting for low exposure, exposure bracketing, RAW format, and increased photo redundancy. Models with Meshroom (Alicevision). Our focus was on visual plausibility rather than quantitative metrics. Results indicated that using RAW format with exposure bracketing and low exposure significantly improved 3D models by reducing artificial edges seen with standard JPG images, despite some noise. A redundant series of RAW photos further reduced edge artifacts and noise, demonstrating the effectiveness of averaging photos to enhance model quality. However, these modifications showed marginal improvements on textured surfaces, underscoring their significant benefits primarily for non-textured surfaces. This study highlights the potential of modified photogrammetry techniques in forensic science, particularly for documenting challenging surfaces. It points out the need for further research, given its limitations in sample size and the absence of extensive parameter testing and quantitative analysis.

Intelligent Classification of JPEG files by Support Vector Machines with Content-based Feature Extraction

Nowadays, multimedia files play a basic role in supporting evidence analysis for making decisions about a crime through looking at files as a digital guide or evidence. Multimedia files such as JPG images are a common format because many documents and memorial images on laptops are valuable. In addition, many JPG images on Laptops are valuable and have fewer structure contents, making recovery possible when their file system is missing. However, intelligent systems for fully recovering corrupted JPG images into their original form is a challenging research issue. In this research, a support vector machine (SVM) as intelligent classifier algorithm is proposed to classify JPG or non-JEG image clusters as part of multimedia files. The SVM classifies the data clusters on three content-based feature extraction (entropy, byte frequency distribution, and rate of change approach to derive cluster features) methods to optimize the identification of JPG image content. The SVM classifier is applied using a radial basis and polynomial kernel functions in MATLAB software. The experimental results show that the accuracy of classification of the SVM classifier with the polynomial function is 96.21%, and the SVM classifier with the radial basis function is 57.58%.

Highly compressed image representation for classification and content retrieval

In this paper, we propose a new method of representing images using highly compressed features for classification and image content retrieval – called PCA-ResFeats. They are obtained by fusing high- and low-level features from the outputs of ResNet-50 residual blocks and applying to them principal component analysis, which leads to a significant reduction in dimensionality. Further on, by applying a floating-point compression, we are able to reduce the memory required to store a single image by up to 1,200 times compared to jpg images and 220 times compared to features obtained by simple output fusion of ResNet-50. As a result, the representation of a single image from the dataset can be as low as 35 bytes on average. In comparison with the classification results on features from fusion of the last ResNet-50 residual block, we achieve a comparable accuracy (no worse than five percentage points), while preserving two orders of magnitude data compression. We also tested our method in the content-based image retrieval task, achieving better results than other known methods using sparse features. Moreover, our method enables the creation of concise summaries of image content, which can find numerous applications in databases.

Double layer steganography technique using DNA sequences and images.

Information security has become increasingly challenging as a result of massive advancements in information and communication technologies. Due to the necessity of exchanging private information and the open nature of the network, there is an increased risk of various types of attacks. Consequently, data security is an essential component of data communication. One of the most effective methods used to achieve secrecy is steganography. This method hides data within a cover object without raising suspicion. The level of security is improved when two steganography methods are combined. This approach is known as multilevel steganography, which hides sensitive data in two cover objects in order to provide a two-level security system. Accordingly, we developed a technique that focuses on protecting secrecy while also being robust to attacks. The new technique uses a multi-layer steganography mechanism by using DNA sequences and images as carriers for sensitive data. The technique intends to hide secret messages in the DNA using the substation algorithm, and then the fake DNA is embedded in an image utilizing the discrete cosine transform (DCT) method. Eventually, the stego image is sent to the intended recipient. Different types of images with different sizes and lengths of messages and DNA sequences were used during the experiments. The results show that the proposed mechanism is resistant to histogram and chi-square attacks. The maximum mean value observed was 0.05, which means the histograms of the original and stego images are nearly identical, and the stego image does not raise any suspicion regarding the existence of secret information. In addition, the imperceptibility ratios were good, as the highest PSNR and MSE values were 0.078 and 72.2, respectively. Finally, the PNG and BMP images show excellent results. On the other hand, the JPG images failed to meet the expected ratio of imperceptibility and security.

Combining raw data and engineered features for optimizing encrypted and compressed internet of things traffic classification

The Internet of Things (IoT) is used in many fields that generate sensitive data, such as healthcare and surveillance. The increased reliance on IoT raised serious information security concerns. As IoT traffic is often compressed to optimize resource consumption, sending compressed data without encryption is a significant security risk for violating confidentiality, integrity, and privacy, as the data can be decompressed, read, and modified. The randomization generated by data compression can complicate classifying compressed data from encrypted data. Encrypted and compressed traffic classification is crucial for the security and forensic analysis of IoT implementations. Existing encrypted and compressed data classification approaches suffer from various limitations. This work proposes two Deep Learning (DL) systems to enhance traffic classification with an emphasis on reducing the possibility of falsely classifying compressed traffic as encrypted. The first approach is Engineered Features Classification (EFC), which uses a set of statistical tests. The second approach is Raw Data and Engineered Features Classification (RDEFC), which combines raw data and statistical tests to improve the classification of encrypted and compressed traffic. Our work also addresses the complexity of classifying encrypted samples of compressed files, such as encrypted JPG images. A large dataset is built in this work with different file types, which include TXT, HTML, WAV, PDF, and JPG. Our evaluation results show high performance for the EFC system with classification 80.94% accuracy. The RDEFC system has a significant improvement over the former with 90.55% classification accuracy. Our approaches outperform systems reported in the literature with similar configurations.

Evaluation of the best Edge Filters in Image Processing Based on the Color Fabric Texture

With the development and complexity of life, the need to improve images appeared, especially when used in the fields of life, including industry and its branches, which affect the life of the citizen, such as the manufacture of fabrics. Which requires precision in the production of these fabrics from the colors and pattern of the fabric. Edge identification is the first step in many digital image-processing applications. Edge identification greatly decreases the data quantity, undesirable filters or unimportant data and provides the important data into the image. There are some issues such as false edge identification, noise issues, low contrast and other edge issues. This paper presents a practical study to compare different edge detectors to determine which edge detector achieves better results, which in turn reflects the best pattern in the fabric. These detectors are Canny, Roberts, Laplace and Gabor. A database of thirty color JPG images collected from the Internet was arranged and a quality scale was used to compare filter detectors. The system MATLAB2020 was used to program the proposed work. The results enhancement was measured by the quality coefficient. This coefficient estimated as follows for Roberts filter (44.27-51.09). Gabor filter (43.46-44.48) and Laplace filter (44.71-5.40). Finally, the quality coefficient for Canny filter equals (44.46-52.05). Therefore, it turns out that the Gabor filter is the best of these filters in defining the edges that were used in defining the pattern.

PreciseEdge raster RGB image segmentation algorithm reduces user input for livestock digital body measurements highly correlated to real-world measurements.

Computer vision is a tool that could provide livestock producers with digital body measures and records that are important for animal health and production, namely body height and length, and chest girth. However, to build these tools, the scarcity of labeled training data sets with uniform images (pose, lighting) that also represent real-world livestock can be a challenge. Collecting images in a standard way, with manual image labeling is the gold standard to create such training data, but the time and cost can be prohibitive. We introduce the PreciseEdge image segmentation algorithm to address these issues by employing a standard image collection protocol with a semi-automated image labeling method, and a highly precise image segmentation for automated body measurement extraction directly from each image. These elements, from image collection to extraction are designed to work together to yield values highly correlated to real-world body measurements. PreciseEdge adds a brief preprocessing step inspired by chromakey to a modified GrabCut procedure to generate image masks for data extraction (body measurements) directly from the images. Three hundred RGB (red, green, blue) image samples were collected uniformly per the African Goat Improvement Network Image Collection Protocol (AGIN-ICP), which prescribes camera distance, poses, a blue backdrop, and a custom AGIN-ICP calibration sign. Images were taken in natural settings outdoors and in barns under high and low light, using a Ricoh digital camera producing JPG images (converted to PNG prior to processing). The rear and side AGIN-ICP poses were used for this study. PreciseEdge and GrabCut image segmentation methods were compared for differences in user input required to segment the images. The initial bounding box image output was captured for visual comparison. Automated digital body measurements extracted were compared to manual measures for each method. Both methods allow additional optional refinement (mouse strokes) to aid the segmentation algorithm. These optional mouse strokes were captured automatically and compared. Stroke count distributions for both methods were not normally distributed per Kolmogorov-Smirnov tests. Non-parametric Wilcoxon tests showed the distributions were different (p< 0.001) and the GrabCut stroke count was significantly higher (p = 5.115 e-49), with a mean of 577.08 (std 248.45) versus 221.57 (std 149.45) with PreciseEdge. Digital body measures were highly correlated to manual height, length, and girth measures, (0.931, 0.943, 0.893) for PreciseEdge and (0.936, 0. 944, 0.869) for GrabCut (Pearson correlation coefficient). PreciseEdge image segmentation allowed for masks yielding accurate digital body measurements highly correlated to manual, real-world measurements with over 38% less user input for an efficient, reliable, non-invasive alternative to livestock hand-held direct measuring tools.

Automated classification of linear bifurcation buckling eigenmodes in thin-walled cylindrical shell structures

Computational problems in structural engineering are growing ever larger and solutions must increasingly be based on correspondingly large datasets obtained from detailed parametric sweeps. However, the acquisition of computational datasets of useful size is also becoming increasingly unfeasible without extensive use of automation. In computational shell buckling studies, particularly those of thin-walled shells under complex loading conditions, an important qualitative piece of information is the class of buckling mode which reveals the dominant destabilising membrane stress components. Unfortunately, the diversity of geometries that can be encountered in computational shell buckling studies is truly vast, and there is currently no way to rapidly assess the buckling mode without laborious direct human observation of the model output.This paper presents an automated classification tool for linear bifurcation buckling eigenmodes in cylindrical shells such as those found as wind turbine support towers, chimneys, silos, tanks, piles and pipelines. It is based on a convolutional neural network implemented using the PyTorch machine learning framework. The adopted network architecture is based on those widely adopted for image classification and recognition tasks, chosen based on a stratified five-fold cross-validation exercise. The network is trained on a purposefully generated basic dataset of 13,392 linear bifurcation buckling eigenmodes modes encoded as chromatic signatures in .jpg images (enhanced to 25,726 by transformations). An example parametric sweep of a cylindrical shell under unsymmetrical wind loading illustrates the performance of the classifier. A GitHub repository offers Python scripts and instructions on how to download the dataset and trained network.

Image enhancement of ultra-low dose CBCT images using a deep generative model

<h3>Objective</h3> The aim of this study was to improve the image quality of CBCT images, obtained with an ultra-low dose (ULD) protocol, using a deep generative model called pix2pix image-to-image translation technique. <h3>Study Design</h3> CBCT images of a dry skull and mandible were obtained using preset standard and ULD imaging protocols. We combined the scans into a dataset with 1,412 ULD scans and normal-dose DICOM image pairs (2,824 scans in total), and randomly selected 10% (140) image pairs as the test set. We trained a pix2pix deep generative model by first converting the DICOM images to JPG images and then feeding the training set image pairs to the network. We tested the performance of the trained model on the previously unseen test set ULD images and compared the synthesized images with the corresponding normal dose and ULD images. A preliminary blind study on the ULD, standard, and synthesized images by an oral and maxillofacial radiologist was done and image quality and edge definition were assessed. <h3>Results</h3> Preliminary results indicated that in the three datasets used for review, all synthesized images were rated as having higher image quality and better edge definition compared to ULD images. Analysis also indicated that synthesized images were equivalent to standard images in image quality and edge definition. <h3>Conclusion</h3> CBCT usage is expected to grow globally by 9.7%. With the expected growth, reducing radiation dose and improving image quality is of paramount importance to reduce the radiation burden on the population. The current study focused on image enhancement of CBCT images acquired with the ULD protocol. Initial analysis indicated a marked improvement in image quality of the synthesized images when compared to ULD images. With further research and successful implementation, we have an opportunity to reduce radiation risks to patients while improving image quality. <b>Statement of Ethical Review</b> Ethical Review or exemption was not warranted for this study.

Dataset: Roundabout Aerial Images for Vehicle Detection

This publication presents a dataset of Spanish roundabouts aerial images taken from a UAV, along with annotations in PASCAL VOC XML files that indicate the position of vehicles within them. Additionally, a CSV file is attached containing information related to the location and characteristics of the captured roundabouts. This work details the process followed to obtain them: image capture, processing, and labeling. The dataset consists of 985,260 total instances: 947,400 cars, 19,596 cycles, 2262 trucks, 7008 buses, and 2208 empty roundabouts in 61,896 1920 × 1080 px JPG images. These are divided into 15,474 extracted images from 8 roundabouts with different traffic flows and 46,422 images created using data augmentation techniques. The purpose of this dataset is to help research into computer vision on the road, as such labeled images are not abundant. It can be used to train supervised learning models, such as convolutional neural networks, which are very popular in object detection.

Dataset: Variable Message Signal Annotated Images for Object Detection

This publication presents a dataset consisting of Spanish road images taken from inside a vehicle, as well as annotations in XML files in PASCAL VOC format that indicate the location of Variable Message Signals within them. Additionally, a CSV file is attached with information regarding the geographic position, the folder where the image is located and the text in Spanish. This can be used to train supervised learning computer vision algorithms such as convolutional neural networks. Throughout this work, the process followed to obtain the dataset, image acquisition and labeling and its specifications are detailed. The dataset constitutes 1216 instances, 888 positives and 328 negatives, in 1152 jpg images with a resolution of 1280 × 720 pixels. These are divided into 756 real images and 756 images created from the data-augmentation technique. The purpose of this dataset is to help in road computer vision research since there is not one specifically for VMSs.

First Successful Rescue of a Lost Person Using the Human Detection System: A Case Study from Beskid Niski (SE Poland)

Recent advances in search and rescue methods include the use of unmanned aerial vehicles (UAVs), to carry out aerial monitoring of terrains to spot lost individuals. To date, such searches have been conducted by human observers who view UAV-acquired videos or images. Alternatively, lost persons may be detected by automated algorithms. Although some algorithms are implemented in software to support search and rescue activities, no successful rescue case using automated human detectors has been reported on thus far in the scientific literature. This paper presents a report from a search and rescue mission carried out by Bieszczady Mountain Rescue Service near the village of Cergowa in SE Poland, where a 65-year-old man was rescued after being detected via use of SARUAV software. This software uses convolutional neural networks to automatically locate people in close-range nadir aerial images. The missing man, who suffered from Alzheimer’s disease (as well as a stroke the previous day) spent more than 24 h in open terrain. SARUAV software was allocated to support the search, and its task was to process 782 nadir and near-nadir JPG images collected during four photogrammetric flights. After 4 h 31 min of the analysis, the system successfully detected the missing person and provided his coordinates (uploading 121 photos from a flight over a lost person; image processing and verification of hits lasted 5 min 48 s). The presented case study proves that the use of an UAV assisted by SARUAV software may quicken the search mission.

Evaluation of Incision Margin and Radiotherapy Adjustment in Breast Cancer-Conserving Surgery Based on VR Pathology 3D Reconstruction.

At present, the incidence of breast cancer is high worldwide and the most effective way to treat breast cancer is surgery. This study mainly explores the application of pathological three-dimensional reconstruction based on VR technology in the evaluation of the margins of breast-conserving surgery and the adjustment of radiotherapy for breast cancer. In the process of making pathological large slices, the slice markers are counted and the upper and outer cutting edges are located and placed in a large embedding box to prevent tissue deformation. ACDSEE6.0 is used to convert the aligned image BMP format into JPG format, the JPG images of each breast are cropped in batches under the same range and size, and then 3DDOCTOR1.0 is imported to edit the images. The boundary edit tool is activated to segment the target area layer by layer to create a boundary data set of the target structure. The stored file is opened and 3D rendering simple surface and volume rendering on the computer is run to display the surface data 3D display and the volume data 3D display. During VR image processing, the data are imported into the VR image workstation and the Radio Dexter software system is used for image adjustment, trimming, and sequence extraction. Then, the preliminary processed CT and MRI data are displayed in pairs. Through multiplane comparison, manual or automatic fusion tools are used to reduce the gap between image sequences. The surgical process is simulated under VR: by changing the window size and viewing angle; using virtual electric drills, cutting tools, and other virtual instruments; ad selecting the operating microscope perspective; and simulating steps such as breast opening, visual field exposure, and tumor resection. The breast tomographic image was reconstructed in multiple planes, and two-dimensional images of any plane could be observed. 10 cases showed high-density shadows and 5 cases showed isodensity shadows. This research contributes to the precise treatment of breast-conserving surgery.

Compression Analysis Using Coiflets, Haar Wavelet, and SVD Methods

The image problem lies in the amount of storage space required, to save memory as little as possible image compression is required. The image compression technique is a technique used to represent an image by reducing the quality of the original image but still retaining the information inside. This study compares the best compression method between Coiflets, Haar wavelets, and SVD with JPG image material. The comparison process has done by calculating the compression ratio (CR), Space Saving (SS), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Peak Signal to Noise Ratio (PSNR). The results obtained prove that the SVD method has the highest compression ratio of 3.25 while in the case of Space Saving (SS) the Coiflets method gives the best performance with a value of 73. Measurement in terms of MSE and RMSE is the best for the Coiflets method because it has an average value. -The smallest average among all methods is 0.02395 and 0.111383. provides the best performance in maintaining compression quality. The best PSNR based image quality assessment is the Coiflets method with the highest PSNR average of 63.02 dB. Overall, the Coiflets, Haar wavelet, and SVD compression methods used for JPG images can reduce file size and preserve image information and quality.

Penetration Test for the 3G Internet Service Provider and Free Basics Service by Facebook

&lt;p&gt;The number of devices connected to the Internet using mobile devices is increasing every day. Charge for mobile data over 3G and 4G networks is high in some countries which pushes users to browse the Internet through text-only service. Facebook proposed free basics service which allows users to browse Facebook for free but without showing images. This research formats the images on web pages in a method that makes images transferred over text-only networks. This allows users with low income or slow connections to surf text service over the internet with images supported. The main objective is to test the free basic service by Facebook and Internet Service Provider over 3G networks additionally proposing an image format for text-only networks. The proposed algorithm converts .png and .jpg images and post them on the Facebook page. The results from the Facebook page show images can be seen over 3G networks with free mode enabled.&lt;/p&gt;

RGSR: A two-step lossy JPG image super-resolution based on noise reduction

Single Image Super-Resolution (SISR) is a fundamental and important low-level computer vision (CV) task, yet its performance on real-world applications is not always satisfactory. Different from the previous SISR research, we focus on a specific but realistic SR issue: How can we obtain satisfied SR results from compressed JPG (C-JPG) images, which is a ubiquitous image format to greatly release storage space while missing fine details. the JPG SR task is deeply analyzed to discover the connotation. Then, we propose an effective two-step model structure named RGSR, involving two specifically designed components, i.e., JPG recovering and SR generation, instead of the perspective of noise elimination in traditional SR approaches. Besides, we further integrate the cycle loss to build a hybrid objective across scales for better SR generation. Experimental results on both of the standard test data sets and real images show that our approach achieves outstanding results and succeed in applying to practical C-JPG SR tasks.

Proposed Technique for Encryption JPG and BMP Images

The technique of chaotic system is considered one of the applications for high security encryption, where a color images sent is encrypted for the purpose of preserving from hacker futility. The color images which are used to study are in both formats JPG and BMP. This paper deals with the method of the numeric encryption key to encrypt each one of the two formats type images and then decrypt the result images using the same key. The measures where used to study the statistical characteristics of result images are the histogram of images, original signal divided by noise signal rate (SNR), maximum squared errors (MSE) and peak signal to the noise rate (PSNR). It is found that the chaotic key with control parameter (r) in rang (3.4567-- 4) and initial value(x) within rang (0.6716 -- 1) make high mixing. Encryption results for BMP color images are very good and efficiently, because of the decryption process given the same input image and have the same pixels values without any loosed values. The result of encryption technique for the color images in JPG format is given a little and acceptable loss because of resaving process for resaving output images using compress data in jpg format algorithm.

AUTHENTICATION BASED IMAGE FORGERY DETECTION USING OPTIMIZED FEATURES IN JPEG IMAGES

With the current development in technology, image manipulation and particularly image splicing has been termed as a regular and established concern. The swift growth in commercial image editing programs and software for instance Adobe Photoshop has significantly elevated the number of forged, doctored and tampered images on a daily basis. This situation has resulted into extreme consequences, minimizing the authenticity and reliability of such images as well as creating untrue beliefs in a wide range of ideal-world relevance. The authenticity of a digital image suffers from severe threats due to the rise of powerful digital image editing tools that easily alter the image contents without leaving any visible traces of such changes. The splicing forgery can be done by copied a one/more region from source image and pasted into a target image to produce a composite image called spliced image. Therefore, this type of forgery is considered very challenging from tampering detection point of view. To make the matter worst some post-processing effects such as blurring, JPEG compression, rotation and scaling maybe introduced in the spliced image. In previous paper, we study found the problems in forgery detection in Jpg images. Robust feature extraction techniques used in block based copy move image forgery detection require a high computation time. There is a need for reduced computation time for detection schemes to be practical for use with large images and in real time environments. Utilization of GPU to compute the processes can highly parallelize the tasks involved to reduce computation time. Current CPU based algorithms that have been designed are not suitable to be directly adopted in a GPU based scheme. It must be determined how a parallel copy move image forgery detection scheme can be designed for use with a GPU. Digital images provide a new way to represent pictures and scenes that only film and a darkroom could supply before. In this research work, we implement the feature extraction using principle component analysis and optimization (ant colony optimization) algorithm to detect the forgery image in JPG images. In optimization approach to classify the features and match the training feature if training and testing features has matching then detect the forgery image in the jpg images. Evaluate the performance parameters PSNR (Peak Signal to Noise Ratio), Error rate Accuracy and compare the existing parameters i.e accuracy. Keywords- Image Forgery, Robust feature extraction, Ant colony Approach and Neural Network