Feature Selection using Genetic Algorithms

With the large amount of data of different types that are available today, the number of features that can be extracted from it is huge. The ever-increasing popularity of multimedia applications, has been a major factor for this, especially in the case of image data. Image data is used for several applications such as classification, retrieval, object recognition, and annotation. Often, utilizing the entire feature set for each of these activities can be not only be time consuming but can also negatively impact the performance. Given the large number of features, it is difficult to find the subset of features that is useful for a given task. Genetic Algorithms (GA) can be used to alleviate this problem, by searching the entire feature set, for those features that are not only essential but improve performance as well. In this project, we explore the various approaches to use GA to select features for different applications, and develop a solution that uses a reduced feature set (selected by GA) to classify images based on their domain/genre. The increased interest in Machine Learning applications has led to the design and development of multiple classification algorithms. In this project, we explore 3 such classification algorithms – Random Forest (RF), Support Vector Machine (SVM), and Neural Networks (NN), and perform 10-fold cross-validation with all 3 methods. The idea is to evaluate the performance of each classifier with the reduced feature set and analyze the impact of feature selection on the accuracy of the model. It is observed that the RF is insensitive to feature selection, while SVM and NN show considerable improvement in accuracy with the reduced feature set. ii The use of this solution is demonstrated in image retrieval, and a possible application in image tampering detection is introduced

A Robust Approach Towards Distinguishing Natural and Computer Generated Images using Multi-Colorspace fused and Enriched Vision Transformer

The works in literature classifying natural and computer generated images are mostly designed as binary tasks either considering natural images versus computer graphics images only or natural images versus GAN generated images only, but not natural images versus both classes of the generated images. Also, even though this forensic classification task of distinguishing natural and computer generated images gets the support of the new convolutional neural networks and transformer based architectures that can give remarkable classification accuracies, they are seen to fail over the images that have undergone some post-processing operations usually performed to deceive the forensic algorithms, such as JPEG compression, gaussian noise, etc. This work proposes a robust approach towards distinguishing natural and computer generated images including both, computer graphics and GAN generated images using a fusion of two vision transformers where each of the transformer networks operates in different color spaces, one in RGB and the other in YCbCr color space. The proposed approach achieves high performance gain when compared to a set of baselines, and also achieves higher robustness and generalizability than the baselines. The features of the proposed model when visualized are seen to obtain higher separability for the classes than the input image features and the baseline features. This work also studies the attention map visualizations of the networks of the fused model and observes that the proposed methodology can capture more image information relevant to the forensic task of classifying natural and generated images

Fast Face-swap Using Convolutional Neural Networks

We consider the problem of face swapping in images, where an input identity is transformed into a target identity while preserving pose, facial expression, and lighting. To perform this mapping, we use convolutional neural networks trained to capture the appearance of the target identity from an unstructured collection of his/her photographs.This approach is enabled by framing the face swapping problem in terms of style transfer, where the goal is to render an image in the style of another one. Building on recent advances in this area, we devise a new loss function that enables the network to produce highly photorealistic results. By combining neural networks with simple pre- and post-processing steps, we aim at making face swap work in real-time with no input from the user

Detection of AI-created images using pixel-wise feature extraction and convolutional neural networks

Generative AI has gained enormous interest nowadays due to new applications like ChatGPT, DALL E, Stable Diffusion, and Deepfake. In particular, DALL E, Stable Diffusion, and others (Adobe Firefly, ImagineArt, etc.) can create images from a text prompt and are even able to create photorealistic images. Due to this fact, intense research has been performed to create new image forensics applications able to distinguish between real captured images and videos and artificial ones. Detecting forgeries made with Deepfake is one of the most researched issues. This paper is about another kind of forgery detection. The purpose of this research is to detect photorealistic AI-created images versus real photos coming from a physical camera. Id est, making a binary decision over an image, asking whether it is artificially or naturally created. Artificial images do not need to try to represent any real object, person, or place. For this purpose, techniques that perform a pixel-level feature extraction are used. The first one is Photo Response Non-Uniformity (PRNU). PRNU is a special noise due to imperfections on the camera sensor that is used for source camera identification. The underlying idea is that AI images will have a different PRNU pattern. The second one is error level analysis (ELA). This is another type of feature extraction traditionally used for detecting image editing. ELA is being used nowadays by photographers for the manual detection of AI-created images. Both kinds of features are used to train convolutional neural networks to differentiate between AI images and real photographs. Good results are obtained, achieving accuracy rates of over 95%. Both extraction methods are carefully assessed by computing precision/recall and F1-score measurements