Machine learning based digital image forensics and steganalysis

The security and trustworthiness of digital images have become crucial issues due to the simplicity of malicious processing. Therefore, the research on image steganalysis (determining if a given image has secret information hidden inside) and image forensics (determining the origin and authenticity of a given image and revealing the processing history the image has gone through) has become crucial to the digital society. In this dissertation, the steganalysis and forensics of digital images are treated as pattern classification problems so as to make advanced machine learning (ML) methods applicable. Three topics are covered: (1) architectural design of convolutional neural networks (CNNs) for steganalysis, (2) statistical feature extraction for camera model classification, and (3) real-world tampering detection and localization. For covert communications, steganography is used to embed secret messages into images by altering pixel values slightly. Since advanced steganography alters the pixel values in the image regions that are hard to be detected, the traditional ML-based steganalytic methods heavily relied on sophisticated manual feature design have been pushed to the limit. To overcome this difficulty, in-depth studies are conducted and reported in this dissertation so as to move the success achieved by the CNNs in computer vision to steganalysis. The outcomes achieved and reported in this dissertation are: (1) a proposed CNN architecture incorporating the domain knowledge of steganography and steganalysis, and (2) ensemble methods of the CNNs for steganalysis. The proposed CNN is currently one of the best classifiers against steganography. Camera model classification from images aims at assigning a given image to its source capturing camera model based on the statistics of image pixel values. For this, two types of statistical features are designed to capture the traces left by in-camera image processing algorithms. The first is Markov transition probabilities modeling block-DCT coefficients for JPEG images; the second is based on histograms of local binary patterns obtained in both the spatial and wavelet domains. The designed features serve as the input to train support vector machines, which have the best classification performance at the time the features are proposed. The last part of this dissertation documents the solutions delivered by the author’s team to The First Image Forensics Challenge organized by the Information Forensics and Security Technical Committee of the IEEE Signal Processing Society. In the competition, all the fake images involved were doctored by popular image-editing software to simulate the real-world scenario of tampering detection (determine if a given image has been tampered or not) and localization (determine which pixels have been tampered). In Phase-1 of the Challenge, advanced steganalysis features were successfully migrated to tampering detection. In Phase-2 of the Challenge, an efficient copy-move detector equipped with PatchMatch as a fast approximate nearest neighbor searching method were developed to identify duplicated regions within images. With these tools, the author’s team won the runner-up prizes in both the two phases of the Challenge

Pixels-off: Data-augmentation Complementary Solution for Deep-learning Steganalysis

Virtual ConferenceInternational audienceAfter 2015, CNN-based steganalysis approaches have started replacing the two-step machine-learning-based steganalysis approaches (feature extraction and classification), mainly due to the fact that they offer better performance.In many instances, the performance of these networks depend on the size of the learning database. Until a certain point, the larger the database, the better the results. However, working with a large database with controlled acquisition conditions is usually rare or unrealistic in an operational context. An easy and efficient approach is thus to augment the database, in order to increase its size, and therefore to improve the efficiency of the steganalysis process. In this article, we propose a new way to enrich a database in order to improve the CNN-based steganalysis performance. We have named our technique "pixels-off". This approach is efficient, generic, and is usable in conjunction with other data-enrichment approaches. Additionally, it can be used to build an informed database that we have named "Side-Channel-Aware databases" (SCA-databases)

Downregulation of CDC20 Increases Radiosensitivity through Mcl-1/p-Chk1-Mediated DNA Damage and Apoptosis in Tumor Cells

Radiotherapy is an important modality for the local control of human cancers, but the radioresistance induced by aberrant apoptotic signaling is a hallmark of cancers. Restoring the aberrant apoptotic pathways is an emerging strategy for cancer radiotherapy. In this study, we determined that targeting cell division cycle 20 (CDC20) radiosensitized colorectal cancer (CRC) cells through mitochondrial-dependent apoptotic signaling. CDC20 was overexpressed in CRC cells and upregulated after radiation. Inhibiting CDC20 activities genetically or pharmacologically suppressed the proliferation and increased radiation-induced DNA damage and intrinsic apoptosis in CRC cells. Mechanistically, knockdown of CDC20 suppressed the expression of antiapoptotic protein Mcl-1 but not other Bcl-2 family proteins. The expressions of CDC20 and Mcl-1 respond to radiation simultaneously through direct interaction, as evidenced by immunoprecipitation and glutathione S-transferase (GST) pull-down assays. Subsequently, decreased Mcl-1 expression inhibited the expression level of phosphorylated checkpoint kinase 1 (p-Chk1), thereby resulting in impaired DNA damage repair through downregulating the homologous recombination repair protein Rad51 and finally causing apoptotic signaling. In addition, both CDC20 and Chk1 inhibitors together, through in vivo studies, confirmed the radiosensitizing effect of CDC20 via inhibiting Mcl-1 and p-Chk1 expression. In summary, our results indicate that targeting CDC20 is a promising strategy to improve cancer radiotherapy

Pan-Cancer Analysis of Radiotherapy Benefits and Immune Infiltration in Multiple Human Cancers

Response to radiotherapy (RT) in cancers varies widely among patients. Therefore, it is very important to predict who will benefit from RT before clinical treatment. Consideration of the immune tumor microenvironment (TME) could provide novel insight into tumor treatment options. In this study, we investigated the link between immune infiltration status and clinical RT outcome in order to identify certain leukocyte subsets that could potentially influence the clinical RT benefit across cancers. By integrally analyzing the TCGA data across seven cancers, we identified complex associations between immune infiltration and patients RT outcomes. Besides, immune cells showed large differences in their populations in various cancers, and the most abundant cells were resting memory CD4 T cells. Additionally, the proportion of activated CD4 memory T cells and activated mast cells, albeit at low number, were closely related to RT overall survival in multiple cancers. Furthermore, a prognostic model for RT outcomes was established with good performance based on the immune infiltration status. Summarized, immune infiltration was found to be of significant clinical relevance to RT outcomes. These findings may help to shed light on the impact of tumor-associated immune cell infiltration on cancer RT outcomes, and identify biomarkers and therapeutic targets