Datasets, Clues and State-of-the-Arts for Multimedia Forensics: An Extensive Review

With the large chunks of social media data being created daily and the parallel rise of realistic multimedia tampering methods, detecting and localising tampering in images and videos has become essential. This survey focusses on approaches for tampering detection in multimedia data using deep learning models. Specifically, it presents a detailed analysis of benchmark datasets for malicious manipulation detection that are publicly available. It also offers a comprehensive list of tampering clues and commonly used deep learning architectures. Next, it discusses the current state-of-the-art tampering detection methods, categorizing them into meaningful types such as deepfake detection methods, splice tampering detection methods, copy-move tampering detection methods, etc. and discussing their strengths and weaknesses. Top results achieved on benchmark datasets, comparison of deep learning approaches against traditional methods and critical insights from the recent tampering detection methods are also discussed. Lastly, the research gaps, future direction and conclusion are discussed to provide an in-depth understanding of the tampering detection research arena

Face Recognition with Attention Mechanisms

Face recognition has been widely used in people’s daily lives due to its contactless process and high accuracy. Existing works can be divided into two categories: global and local approaches. The mainstream global approaches usually extract features on whole faces. However, global faces tend to suffer from dramatic appearance changes under the scenarios of large pose variations, heavy occlusions, and so on. On the other hand, since some local patches may remain similar, they can play an important role in such scenarios. Existing local approaches mainly rely on cropping local patches around facial landmarks and then extracting corresponding local representations. However, facial landmark detection may be inaccurate or even fail, which would limit their applications. To address this issue, attention mechanisms are applied to automatically locate discriminative facial parts, while suppressing noisy parts. Following this motivation, several models are proposed, including: the Local multi-Scale Convolutional Neural Networks (LS-CNN), Hierarchical Pyramid Diverse Attention (HPDA) networks, Contrastive Quality-aware Attentions (CQA-Face), Diverse and Sparse Attentions (DSA-Face), and Attention Augmented Networks (AAN-Face). Firstly, a novel spatial attention (local aggregation networks, LANet) is proposed to adaptively locate useful facial parts. Meanwhile, different facial parts may appear at different scales due to pose variations and expression changes. In order to solve this issue, LS-CNN are proposed to extract discriminative local information at different scales. Secondly, it is observed that some important facial parts may be neglected, if without a proper guidance. Besides, hierarchical features from different layers are not fully exploited which can contain rich low-level and high-level information. To overcome these two issues, HPDA are proposed. Specifically, a diverse learning is proposed to enlarge the Euclidean distances between each two spatial attention maps, locating diverse facial parts. Besides, hierarchical bilinear pooling is adopted to effectively combine features from different layers. Thirdly, despite the decent performance of the HPDA, the Euclidean distance may not be flexible enough to control the distances between each two attention maps. Further, it is also important to assign different quality scores for various local patches because various facial parts contain information with various importance, especially for faces with heavy occlusions, large pose variations, or quality changes. The CQA-Face is proposed which mainly consists of the contrastive attention learning and quality-aware networks where the former proposes a better distance function to enlarge the distances between each two attention maps and the latter applies a graph convolutional network to effectively learn the relations among different facial parts, assigning higher quality scores for important patches and smaller values for less useful ones. Fourthly, the attention subset problem may occur where some attention maps are subsets of other attention maps. Consequently, the learned facial parts are not diverse enough to cover every facial detail, leading to inferior results. In our DSA-Face model, a new pairwise self-constrastive attention is proposed which considers the complement of subset attention maps in the loss function to address the aforementioned attention subset problem. Moreover, a attention sparsity loss is proposed to suppress the responses around noisy image regions, especially for masked faces. Lastly, in existing popular face datasets, some characteristics of facial images (e.g. frontal faces) are over-represented, while some characteristics (e.g. profile faces) are under-represented. In AAN-Face model, attention erasing is proposed to simulate various occlusion levels. Besides, attention center loss is proposed to control the responses on each attention map, guiding it to focus on the similar facial part. Our works have greatly improved the performance of cross-pose, cross-quality, cross-age, cross-modality, and masked face matching tasks