Image forgery detection is crucial in digital forensics, cybersecurity, and legal investigations. Despite advancement, detecting subtle manipulations like copy-move forgeries remains challenging due to increasingly realistic images. This paper proposes a hybrid approach that combines a pretrained VGG16 model for feature extraction, cosine similarity for block-level comparison, and support vector machines for classification, addressing key limitations of existing methods. Through a comparative evaluation of CNN architectures, VGG16 is identified as the most effective for extracting discriminative features in this context. Cosine similarity quantifies the similarity between image block features to enable the model to focus more effectively on the tampered regions that closely resemble the original ones, and SVMs are leveraged to classify authentic versus forged regions. This novel integration of deep learning for feature extraction and classical classification techniques is highly accurate with minimal false positives, without relying on handcrafted features. Experiments on the MICC-F2000 dataset demonstrate the method’s strong performance, achieving 99.59% precision, 98.00% recall, and a 0.99 F1-score.

Collaborative robots (cobots) are revolutionizing industries, warfare, and smart cities. Multimodal data fusion (MMDF) enables a smart system to predict or decide based on the basis of multiple sensor input modalities. This survey comprehensively reviews MMDF techniques and their use cases for various cobots architectures and general approaches for multi-robot cooperation. By addressing future directions and challenges such as real-time processing and robustness in dynamic environments and identifying open research questions, this survey aims to guide future developments in the field, fostering innovation in robot collaboration.

MobileNet is a lightweight convolutional neural network optimized for resource-limited environments. However, its use of depthwise separable convolutions to minimize parameters and computation can reduce accuracy due to oversimplified channel interactions. Principal Component Analysis (PCA) can address this issue by reducing the dimensionality of weight matrices while preserving key features. Applying PCA can help maintain accuracy and compress the model simultaneously. Based on this, we propose a novel copy-move forgery detection approach based on MobilNetV2 and PCA for feature extraction, and a random forest for classification. This allows us to enhance MobileNet accuracy while keeping its model size compact. The results of our experiments conducted on the MICC-F2000 dataset reveal that the proposed hybrid lightweight model outperforms the individual transfer learning structures and the existing literature, achieving 96.37% accuracy.

With the advancement and widespread use of digital devices, capturing images has become effortless in any location. Images serve as evidence, making the authenticity of digital images increasingly critical. Some individuals alter images by adding or removing elements, rendering the images unreliable. Consequently, detecting image forgery has become essential. The evolution of image editing software has intensified this issue within the realm of computer vision. Recently, a variety of algorithms have been developed to identify image forgery. However, with the progress in digital technology, the ease of image manipulation has led to a surge in forgery cases, presenting significant obstacles when verifying their authenticity. Thus, there is a pressing requirement for effective forgery detection methods. This paper introduces a novel copy-move forgery detection approach based on the fusion of a handcrafted forgery detection system utiliszing the scale-invariant feature transform and the support vector machine with a deep-learning-based forgery detection system using VGG16. The aim is to address the challenges of subtle forgery detection and blending and seamless Integration. We conduct the experiments on the MICC_F2000 image manipulation dataset and assess the efficacy of the proposed approach, achieving $\mathbf{9 6 . 7 5 \%}$ accuracy, $\mathbf{1 0 0 \%}$ precision and $95.5 \%$ F1-score. This research demonstrates superior performance compared to state-of-the-art methods.

The importance of ensuring the authenticity and reliability of digital images has grown significantly, primarily due to the ease of modifying such images with the progress of digital image editing tools. Consequently, there is a growing emphasis on the development of techniques for detecting image manipulation. One particular area of focus in digital image authentication is copy-move forgery detection. This paper presents a survey and a comparative study on copy-move forgery detection techniques in digital images, databases, and evaluation metrics. The study aims to provide insights into the effectiveness of different methods in detecting copy-move forgeries. The paper discusses prominent detection techniques, including block-based, keypoint-based, transform domain, hybrid methods, deep learning, and GAN approaches. The findings highlight the strengths, weaknesses, and key similarities and differences among the approaches. This study contributes to the understanding of the state-of-the-art in copy-move forgery detection and provides guidance for future research in this field.

The use of biometric technology in authentication and integrity verification systems necessarily gives rise to issues relating to the security and privacy of transmitted and stored templates. Many techniques have been proposed in the literature involving crypto-biometric algorithm, template transformation method and watermarking reinforcement scheme. Watermarking reinforcement relying on biometrics usually combines the watermarking technique with a feature transformation scheme. We present a new watermarking reinforcement scheme including two security levels to protect biometric data. The first security level is proposed to verify the integrity of the fingerprint while transmitted or stored based on a watermarking approach. To avoid any registration of the stored biometric template, a second security level is developed based on cancellable transformation. The watermarking of the fingerprint image is applied in the wavelet packet decomposition multiresolution domain using a binary watermark derived from the fingerprint minutiae. The developed transformation is designed to explore the relative relation between minutiae in the pair-polar coordinate domain. The transformed minutiae satisfy the non-invertibility recovery of the original ones. Under various scenarios, the suggested scheme is evaluated using the public fingerprint database BioSecure and FVC2002 DB1. The realized testing and derived results show the robustness of the first security level under various attacks and the satisfaction of the second level security to protect biometrics template with an insignificant degradation of the authentication performance.

In this paper we propose a new approach for watermarking biometric fingerprint images using Gabor direct linear discriminant analysis face features. Our goal is to incorporate a watermark in the best embedding domain that preserves minutiae, which are the most relevant proven features of a fingerprint. We conducted a comprehensive study based on the influence of the watermark embedding domain choice on the performance of the minutiae-based identity and of the robustness and imperceptibility of the watermarking approach. Three embedding domains were tested: spatial, frequency and multiresolution. The various tests were performed on two biometric databases, multimodal and chimerical. The best results were recorded in the multiresolution domain in terms of preserving the minutiae number and positions. Moreover, this embedding domain led to the best verification performances and to a good compromise between robustness and imperceptibility.

In this paper, we propose a new approach based on watermarking for fusing biometric modalities. The main idea of the proposed approach is to use the watermark both for security purpose and as an additional information related to the person, therefore increasing the personal data used for verification. The host image of the face is watermarked in the multi-resolution space by the palmprint using a watermarking technique based on wavelet packet decomposition. For the verification stage, the characterization of the watermarked face is provided by Gabor filters while classification is performed by SVMs. The experimental results show that this technique ensures a significant performance improvement in both identity verification and biometric security over the use of a single system.