Online review platforms have drastically reshaped how we interact, make purchasing decisions, and engage with digital content. However, the rise of deceptive content, privacy breaches, and misinformation has undermined online content credibility, impacting the trustworthiness of the information shared. To address these issues, we propose a robust framework for automated trust assessment of online reviews, focusing on identifying deceptive online content. The core of our approach is the trust vector, a novel feature representation that captures key user engagement factors influencing content trustworthiness. By applying the Weighted Trust Scoring Method (WTSM), we calculate a weighted trust score that strengthens the model’s interpretability and effectiveness in trust evaluation. The proposed model is evaluated on two benchmark datasets-the Deceptive Opinion Corpus Dataset and the Yelp Review Dataset. The framework achieves classification accuracies of $85 \%$ and $87 \%$, respectively, demonstrating its effectiveness in distinguishing deceptive from truthful content.

In an era of increasing digital privacy risks, images shared online can inadvertently reveal sensitive location data through identifiable elements such as logos, road signs, and text. These disclosures enable unauthorized tracking and data mining, raising serious privacy concerns. This paper proposes a novel framework that combines object detection and generative inpainting for privacy preserving image reconstruction. A Mask R-CNN model is trained on three diverse datasets to detect and segment location identifiable elements accurately. The detected regions are then de-identified using a proposed Denoising Diffusion Probabilistic Model (DDPM)-based inpainting method, which preserves scene integrity by ensuring geometric consistency and natural lighting. Unlike traditional inpainting methods, the proposed framework dynamically refines image reconstructions through controlled denoising, achieving high realism. The effectiveness of the method is evaluated using standard image quality metrics, including PSNR, SSIM, and FID, alongside subjective visual assessments. Experimental results show that the proposed approach outperforms baseline models such as CNNs and GANs, offering a robust solution for privacy preserving image reconstruction.

Soft biometric de-identification is an emerging field in biometrics, offering a balance between privacy protection and recognition accuracy. In this work, we present a novel identity-preserving soft biometric obfuscation method that uses the latent feature space of a trained generator and employs deep neural networks. The proposed method aims at preserving the identity of individuals while de-identifying their soft biometric attributes. Specifically, a novel feature space transformation network is designed to preserve identity while modifying facial attributes while minimizing the disclosure of identity. The proposed feature transformation network is the first of its kind developed specifically for controllable adaptive de-identification. Furthermore, we implemented an identity preservation mechanism, utilizing the FaceNet architecture to compute embedding vectors for both the original and deidentified images. Through extensive validation on benchmark datasets such as VGGFace2 and CelebA, we have demonstrated the effectiveness and robustness of our method.