Video Source Forensics for IoT Devices Based on Convolutional Neural Networks

With the wide application of Internet of things devices and the rapid development of multimedia technology, digital video has become one of the important information dissemination carriers among Internet of things devices, and it has been widely used in many fields such as news media, digital forensics and so on. However, the current video editing technology is constantly developing and improving, which seriously threatens the integrity and authenticity of digital video. Therefore, the research on digital video forensics has a great significance. In this paper, a new video source passive forensics algorithm based on Convolutional Neural Networks(CNN) is proposed. CNN is used to classify the maximum information block of specified size in video I frame, and then the classification results are fused to determine the camera to which the video belongs. Experimental results show that the recognition algorithm proposed in this paper has a better performance than other methods in trems of accuracy and ROC curve. And our method still can have a good recognition effect even if a small number of I frames are used for recognition

Image Source Identification Using Convolutional Neural Networks in IoT Environment

Digital image forensics is a key branch of digital forensics that based on forensic analysis of image authenticity and image content. The advances in new techniques, such as smart devices, Internet of Things (IoT), artificial images, and social networks, make forensic image analysis play an increasing role in a wide range of criminal case investigation. This work focuses on image source identification by analysing both the fingerprints of digital devices and images in IoT environment. A new convolutional neural network (CNN) method is proposed to identify the source devices that token an image in social IoT environment. The experimental results show that the proposed method can effectively identify the source devices with high accuracy

Zwitterionic Injectable Hydrogel-Combined Chemo- and Immunotherapy Medicated by Monomolecular Micelles to Effectively Prevent the Recurrence of Tumor Post Operation

Surgical resection remains the most common method of tumor treatment; however, the high recurrence and metastasis after surgery need to be solved urgently. Herein, we report an injectable zwitterionic hydrogel based on “thiol-ene” click chemistry containing doxorubicin (DOX) and a macrophage membrane (MM)-coated 1-methyl-tryptophan (1-MT)-loaded polyamide-amine dendrimer (P-DOX/1MT) for preventing the postoperative recurrence of tumors. The results indicated that P-DOX/1MT@MM exhibited enhanced recognition and uptake of the dendrimer by tumor cells and induced the immunogenic cell death. In the mice tumor model, the P-DOX/1MT@MM-Gel exhibited high therapeutic efficiency, which could significantly reduce the recurrence of the tumor, including suppressing tumor growth, promoting dendritic cell maturation, and increasing tumor-infiltrating cytotoxic T lymphocytes. The mechanism analysis revealed that the hydrogel greatly reduces the side effects to normal tissues and significantly improves its therapeutic effect. 1MT in the hydrogel is released more rapidly, improving the tumor suppressor microenvironment and increasing the tumor cell sensitivity to DOX. Then, the DOX in the P-DOX/1MT@MM effectively eliminatedo the residual tumor cells and exerted enhanced toxicity. In conclusion, this novel injectable hydrogel that combines chemotherapy and immunotherapy has the property of sequential drug release and is a promising strategy for preventing the postoperative recurrence of tumors