Efficient resampling features and convolution neural network model for image forgery detection

The extended utilization of picture-enhancing or manipulating tools has led to ease of manipulating multimedia data which includes digital images. These manipulations will disturb the truthfulness and lawfulness of images, resulting in misapprehension, and might disturb social security. The image forensic approach has been employed for detecting whether or not an image has been manipulated with the usage of positive attacks which includes splicing, and copy-move. This paper provides a competent tampering detection technique using resampling features and convolution neural network (CNN). In this model range spatial filtering (RSF)-CNN, throughout preprocessing the image is divided into consistent patches. Then, within every patch, the resampling features are extracted by utilizing affine transformation and the Laplacian operator. Then, the extracted features are accumulated for creating descriptors by using CNN. A wide-ranging analysis is performed for assessing tampering detection and tampered region segmentation accuracies of proposed RSF-CNN based tampering detection procedures considering various falsifications and post-processing attacks which include joint photographic expert group (JPEG) compression, scaling, rotations, noise additions, and more than one manipulation. From the achieved results, it can be visible the RSF-CNN primarily based tampering detection with adequately higher accurateness than existing tampering detection methodologies.

Deepfake-Image Anti-Forensics with Adversarial Examples Attacks

Many deepfake-image forensic detectors have been proposed and improved due to the development of synthetic techniques. However, recent studies show that most of these detectors are not immune to adversarial example attacks. Therefore, understanding the impact of adversarial examples on their performance is an important step towards improving deepfake-image detectors. This study developed an anti-forensics case study of two popular general deepfake detectors based on their accuracy and generalization. Herein, we propose the Poisson noise DeepFool (PNDF), an improved iterative adversarial examples generation method. This method can simply and effectively attack forensics detectors by adding perturbations to images in different directions. Our attacks can reduce its AUC from 0.9999 to 0.0331, and the detection accuracy of deepfake images from 0.9997 to 0.0731. Compared with state-of-the-art studies, our work provides an important defense direction for future research on deepfake-image detectors, by focusing on the generalization performance of detectors and their resistance to adversarial example attacks.

Image Forensic Tool (IFT)

Images now-a-days are often used as an authenticated proof for any cyber-crime. Images that do not remain genuine can mislead the court of law. The fast and dynamically growing technology doubts the trust in the integrity of images. Tampering mostly refers to adding or removing important features from an image without leaving any obvious trace. In earlier days, digital signatures were used to preserve the integrity, but now a days various tools are available to tamper digital signatures as well. Even in various state-of-the-art works in tamper detection, there are various restrictions in the type of inputs and the type of tampering detection. In this paper, the researchers propose a prototype model in the form of a tool that will retrieve all the image files from given digital evidence and detect tampering in the images. For various types of tampering, different tampering detection algorithms have been used. The proposed prototype will detect if tampering has been done or not and will classify the image files into groups based on the type of tampering.

Research note: This photograph has been altered: Testing the effectiveness of image forensic labeling on news image credibility

Despite the ubiquity of images and videos in online news environments, much of the existing research on misinformation and its correction is solely focused on textual misinformation, and little is known about how ordinary users evaluate fake or manipulated images and the most effective ways to label and correct such falsities. We designed a visual forensic label of image authenticity, Picture-O-Meter, and tested the label’s efficacy in relation to its source and placement in an experiment with 2440 participants. Our findings demonstrate that, despite human beings’ general inability to detect manipulated images on their own, image forensic labels are an effective tool for counteracting visual misinformation.

Texture based Image Splicing Forgery Recognition using a Passive Approach

With the growing usage of the internet in daily life along with the usage of dominant picture editing software tools in creating forged pictures effortlessly, make us lose trust in the authenticity of the images. For more than a decade, extensive research is going on in the Image forensic area that aims at restoring trustworthiness in images by bringing various tampering detection techniques. In the proposed method, identification of image splicing technique is introduced which depends on the picture texture analysis which characterizes the picture areas by the content of the texture. In this method, an image is characterized by the regions of their texture content. The experimental outcomes describe that the proposed method is effective to identify spliced picture forgery with an accuracy of 79.5%.