Membership Inference Attacks on DNNs using Adversarial Perturbations

Several membership inference (MI) attacks have been proposed to audit a target DNN. Given a set of subjects, MI attacks tell which subjects the target DNN has seen during training. This work focuses on the post-training MI attacks emphasizing high confidence membership detection -- True Positive Rates (TPR) at low False Positive Rates (FPR). Current works in this category -- likelihood ratio attack (LiRA) and enhanced MI attack (EMIA) -- only perform well on complex datasets (e.g., CIFAR-10 and Imagenet) where the target DNN overfits its train set, but perform poorly on simpler datasets (0% TPR by both attacks on Fashion-MNIST, 2% and 0% TPR respectively by LiRA and EMIA on MNIST at 1% FPR). To address this, firstly, we unify current MI attacks by presenting a framework divided into three stages -- preparation, indication and decision. Secondly, we utilize the framework to propose two novel attacks: (1) Adversarial Membership Inference Attack (AMIA) efficiently utilizes the membership and the non-membership information of the subjects while adversarially minimizing a novel loss function, achieving 6% TPR on both Fashion-MNIST and MNIST datasets; and (2) Enhanced AMIA (E-AMIA) combines EMIA and AMIA to achieve 8% and 4% TPRs on Fashion-MNIST and MNIST datasets respectively, at 1% FPR. Thirdly, we introduce two novel augmented indicators that positively leverage the loss information in the Gaussian neighborhood of a subject. This improves TPR of all four attacks on average by 2.5% and 0.25% respectively on Fashion-MNIST and MNIST datasets at 1% FPR. Finally, we propose simple, yet novel, evaluation metric, the running TPR average (RTA) at a given FPR, that better distinguishes different MI attacks in the low FPR region. We also show that AMIA and E-AMIA are more transferable to the unknown DNNs (other than the target DNN) and are more robust to DP-SGD training as compared to LiRA and EMIA

Editorial: EBV-Associated Carcinomas: Presence, Role, and Prevention Strategies.

This special issue addresses an important topic related to the role of Epstein-Barr virus (EBV) in human carcinomas initiation and progression, which is one of the most common viral infections worldwide. Today, the relationship between EBV infection and several types of human lymphomas is clearly established, including Hodgkin and Burkitt's lymphoma; meanwhile, it was recently pointed out that EBV is present in nasopharyngeal carcinomas as well as other epithelial cancers (1). EBV is ubiquitous human herpesvirus 4, its genome codes more than 85 proteins of which only few are well-understood; More specifically, six nuclear antigens (EBNA: 1, 2, 3A, 3B, 3C, and LP); three latent membrane proteins/genes (LMP: 1, 2A, 2B) as well as small non-polyadenylated RNAs, EBERs 1 and 2 in addition to few microRNAs have been identified so far, as key regulators, of the oncogenic activity of this virus (2, 3). Present estimates indicate that EBV causes 200,000 new cancer cases annually, accounting for ~2% of cancers worldwide (Cancer Research UK). On the other hand, it is important to emphasize that recent investigations have revealed the possible involvement of EBV in other cancers such as cervical, gliomas, and breast, which are highlighted in this issue.This work is supported by Qatar University grants# GCC-2017-002 QU/KU and QUCG-CMED-20182019-3

R2S100K: Road-Region Segmentation Dataset For Semi-Supervised Autonomous Driving in the Wild

Semantic understanding of roadways is a key enabling factor for safe autonomous driving. However, existing autonomous driving datasets provide well-structured urban roads while ignoring unstructured roadways containing distress, potholes, water puddles, and various kinds of road patches i.e., earthen, gravel etc. To this end, we introduce Road Region Segmentation dataset (R2S100K) -- a large-scale dataset and benchmark for training and evaluation of road segmentation in aforementioned challenging unstructured roadways. R2S100K comprises 100K images extracted from a large and diverse set of video sequences covering more than 1000 KM of roadways. Out of these 100K privacy respecting images, 14,000 images have fine pixel-labeling of road regions, with 86,000 unlabeled images that can be leveraged through semi-supervised learning methods. Alongside, we present an Efficient Data Sampling (EDS) based self-training framework to improve learning by leveraging unlabeled data. Our experimental results demonstrate that the proposed method significantly improves learning methods in generalizability and reduces the labeling cost for semantic segmentation tasks. Our benchmark will be publicly available to facilitate future research at https://r2s100k.github.io/

The co-presence of high-risk human papillomaviruses and Epstein-Barr virus is linked with tumor grade and stage in Qatari women with breast cancer.

High-risk human papillomaviruses (HPV) can be present and cooperate with Epstein-Barr virus (EBV) to promote the onset and/or progression of various cancers including cervical, breast, head and neck as well as colorectal. In this investigation, we explored the co-prevalence of high-risk HPV and EBV in 74 breast cancer tissues from Qatari women using polymerase chain reaction. We found that high-risk HPV and EBV are present in 48/74 (65%) and 36/74 (49%) of the cases, respectively. While we noted that the presence of HPV presence is associated with triple-negative breast cancer (TNBC) ( = .008), however, the presence of EBV did not correlate with any breast cancer subgroup. Moreover, our data revealed that high-risk HPV and EBV are co-present in 35/74 (47%) of the samples and their co-presence is significantly associated with tumor grade ( = .04) and tumor stage ( = .04). These data indicate that HPV and EBV are commonly co-present in breast cancer and their association could be linked with a more aggressive tumor phenotype. Thus, further investigations are essential to understand the underlying mechanisms of HPV and EBV cooperation in breast carcinogenesis.grants from Qatar University [QUCG-CMED-2018/2019-3, QUHI-CMED-19/20-1 and QUCG-CMED-20/21-

Delayed Recovery of Contrast Induced Kidney Injury Post Angiography: Rate and Risk Factors

Background: Exposure to Contrast media is the third leading cause of hospital acquired acute kidney injury. It follows a predictable time of onset and a less predictable scenario in recovery. This is related to certain factors, but at the end there will be asubstantial association with increased mortality, morbidity, and length of hospitalization. Objectives: To define the Incidence of persistent contrast induced renal impairment a month after angiography and to define the risk factors for such persistence Patients and Methods: One hundred and one patients (101) were enrolled in this study. All were referred to the Iraqi Center for Heart Diseases in Baghdad/ Iraq for coronary and/or peripheral vascular angiography from October 2009 to July 2010. All patients’ clinical and laboratory data including baseline renal function tests were ordered and recorded with subsequent risk categorization. Post procedure serum creatinine checked on regular intervals (48 hours, one week, and then weekly for three months for those with serum creatinine consistent with the contrast induced - acute kidney injury (CI-AKI) definition at 48 hours. Results: The mean age for the study group was 62±16.9 with 78 (77%) male. Twenty three patients (22.7%) had diabetes mellitus. Thirteen patients (12.7%)had a pre-existing renal impairment. Twenty one patients (20.7%) received extradoses of contrast. Thirty two patients (31.6%) developed CIN by the definition within 48 hours of the procedure with the mean serum creatinine of 1.8 mg/dl. Seven patients (6.8%) continued to have impaired renal function at week 4 and persisted to have such low GFR up to three months of the procedure. Baseline low GFR, Diabetes mellitus and extradoses of contrast were independent risk factors for the occurrence of CIN and delayed renal recovery. Conclusion: The risk of poor renal recovery after contrast cannot be overlooked. Preexisting renal function impairment, diabetes mellitus, and extra doses of contrast media are independent risk factors for such delay