The research on forgery detection and localization is significant in digital forensics and has attracted increasing attention recently. Traditional methods mostly use handcrafted or shallow-learning based features, but they have limited description ability and heavy computational costs. Recently, deep neural networks have shown to be capable of extracting complex statistical features from high-dimensional inputs and efficiently learning their hierarchical representations. In order to capture more discriminative features between tampered and non-tampered regions, we propose an improved mask regional convolutional neural network (Mask R-CNN) which attach a Sobel filter to the mask branch of Mask R-CNN in this paper. The Sobel filter acts as an auxiliary task to encourage predicted masks to have similar image gradients to the groundtruth mask. The overall network is capable of detecting two different types of image manipulations, including copy-move and splicing. The experimental results on two standard datasets show that the proposed model outperforms some state-of-the-art methods.

Internet of Things (IoT) is one of the rapidly developing technologies today that attract huge real-world applications. However, the reality is that IoT is easily vulnerable to numerous types of cyberattacks and anomalies. Detecting them is becoming increasingly challenging day by day due to limitations with IoT devices and threat intelligence. Particularly, one of the most challenging problems is to detect the existence of malicious adversaries that continuously adapt or conceal their behaviors in IoT to hide their actions and to make the IoT security protocol ineffective. In this article, we study this problem at the IoT device level that can be a great idea to avoid potential attacks. We present AntiConcealer , an edge-aided IoT framework, and propose an edge artificial intelligence-enabled approach (EdgeAI) for detecting adversary concealed behaviors in the IoT. We first develop an adversary behavior model and use this to identify mid-attack temporal patterns by learning the multivariate Hawkes process (MHP), a kind of point process as a random and finite series of events (e.g., behaviors) controlled by a probabilistic model. Naturally, learning MHP processed on EdgeAI reveals the influence of the concealed behaviors of adversaries in the IoT. These concealed behaviors are then grouped using a nonnegative weighted influence matrix. To observe the performance of the AntiConcealer framework through evaluation, we employ honeypots integrated with edge servers and verify the usability and reliability of adversary behavioral identification.

Mobile target tracking with artificial intelligence (AI) approaches such as deep reinforcement learning (DRL) in edge-assisted Internet of Things (Edge-IoT) platform can be promising. In this article, we propose DRLTrack , a framework for target tracking with a collaborative DRL called C-DRL in Edge-IoT with the aim to obtain two major objectives: high quality of tracking (QoT) and resource-efficient network performance. In DRLTrack , a huge number of IoT devices are employed to collect data about a target of interest. One or two edge devices in the network coordinate with a group of IoT devices and collaboratively detect the target by using the C-DRL approach and form an area around the target by the group of IoT devices. To maintain such an area during the tracking time, we employ a deep Q-network to track the target from one group to another. An EdgeAI sitting on the top of the edge devices has the control of the C-DRL approach during tracking and can identify a sequence of tracks. DRLTrack is said to be trustworthy as it shows trustworthy performance in terms of QoT, dynamic environments, and even under certain cyberattacks. We validate the performance of DRLTrack considering the objectives through simulations and it demonstrates superior performance compared with existing work.

Tampered images can easily be used for illegal activities, such as spreading rumors, economic fraud, fabricating false news, and illegally obtaining experience benefits, etc . With the improvement and development of artificial intelligence (AI), image manipulation technology has also been further improved, more and more retouching software in daily life adopts AI technology. So far, there is no AI-based tampered dataset. To address this challenge, we propose a dataset-IPM15K. It utilizes the most advanced image processing technology and contains a total of 150,00 doctored vital images. This dataset also could serve as a catalyst for progressing many vision tasks, e.g ., localization, segmentation, and alpha-matting, etc . Additionally, we propose an effective multi-feature fusion identification network (MFI-Net) to identify these challenging images. Our model consists of four modules: the detail extraction module (DEM), which utilizes different sizes of convolutions and perceptual fields to extract more valuable information of tampered locations; the multi-branch attention fusion module (MAFM), which fully exploits contextual information of different levels to capture subtle traces of tampering; the feature decoder component (FDC), which combines fused features to identify tampered regions; and the detail enhancement block (DEB), which continues to supplement the detailed information of the detected regions. Extensive experiments on three public datasets and the proposed dataset show that MFI-Net outperforms various state-of-the-art (SOTA) manipulation detection baselines.