Reverse Engineering of Adversarial Samples by Leveraging Patterns left by the Attacker

Intrinsic susceptibility of deep learning to adversarial examples has led to a plethora of attack techniques with a common broad objective of fooling deep models. However, we find slight compositional differences between the algorithms achieving this objective. These differences leave traces that provide important clues for attacker profiling in real-life scenarios. Inspired by this, we introduce a novel problem of \u27Reverse Engineering of aDversarial attacks\u27 (RED). Given an adversarial example, the objective of RED is to identify the attack used to generate it. Under this perspective, we can systematically group existing attacks into different families, leading to the sub-problem of attack family identification. To enable RED analysis, we introduce a large \u27Adversarial Identification Dataset\u27 (AID), comprising over 180k adversarial samples generated with 13 popular attacks for image specific/agnostic white/black box setups. We use AID to devise a novel framework for the RED objective. The proposed framework is designed using a novel Transformer based Global-LOcal Feature(GLoF) module which helps in approximating the adversarial perturbation and identification of the attack. Using AID and our framework, we provide multiple interesting benchmark results for the RED problem

PRAT: PRofiling Adversarial aTtacks

Intrinsic susceptibility of deep learning to adversarial examples has led to a plethora of attack techniques with a broad common objective of fooling deep models. However, we find slight compositional differences between the algorithms achieving this objective. These differences leave traces that provide important clues for attacker profiling in real-life scenarios. Inspired by this, we introduce a novel problem of PRofiling Adversarial aTtacks (PRAT). Given an adversarial example, the objective of PRAT is to identify the attack used to generate it. Under this perspective, we can systematically group existing attacks into different families, leading to the sub-problem of attack family identification, which we also study. To enable PRAT analysis, we introduce a large Adversarial Identification Dataset (AID), comprising over 180k adversarial samples generated with 13 popular attacks for image specific/agnostic white/black box setups. We use AID to devise a novel framework for the PRAT objective. Our framework utilizes a Transformer based Global-LOcal Feature (GLOF) module to extract an approximate signature of the adversarial attack, which in turn is used for the identification of the attack. Using AID and our framework, we provide multiple interesting benchmark results for the PRAT problem

Hardware Implementation of SpoC-128

In this work, we present a hardware implementation of the lightweight Authenticated Encryption with Associated Data (AEAD) SpoC-128. Designed by AlTawy, Gong, He, Jha, Mandal, Nandi and Rohit; SpoC-128 was submitted to the Lightweight Cryptography (LWC) competition being organised by the National Institute of Standards and Technology (NIST) of the United States Department of Commerce. Our implementation follows the Application Programming Interface (API) specified by the cryptographic engineering research group in the George Mason University (GMU). The source codes are available over the public internet as an open-source project

Assessment of genetic diversity and development of core germplasm in durum wheat using agronomic and grain quality traits

Genetic resources are the fundamental source of diversity to plant breeders for improvement of desired traits. However, large germplasm set is difcult to preserve and use as working collection in genetic studies, hence in the present study evaluation of genetic diversity of 604 durum germplasm originated from diferent geographical areas and development of core collection for representation of diverse germplasm for working collection was done. Six hundred and four durum germplasm were sown in augmented design and data were recorded for eight quantitative characters including agronomic and grain quality traits. Descriptive statistics showed large variation for all studied traits. Box plot analysis for nine diferent sets of germplasm showed a large variation for agronomic traits and quality traits. Principal component analysis for the frst four principal components explained 71% of the cumulative variation and grouped germplasm in the main two groups. The core germplasm set was developed using corehunter package with 10% cutof and hence 60 germplasm entries make representation in core subset based on maximum diversity and minimum redundancy. The maximum contribution to core germplasm was by each CZ (Central Zone) breeding lines and D numbers (23%) followed by exotic other lines (20%). The developed core subset was validated using multivariate analysis such as Shannon diversity index, comparison of means and homogeneity of error variances using the Levene’s test. The results described in this study will be useful for durum wheat breeders for the development of varieties with high end-use quality

Epigenome environment interactions accelerate epigenomic aging and unlock metabolically restricted epigenetic reprogramming in adulthood.

Our early-life environment has a profound influence on developing organs that impacts metabolic function and determines disease susceptibility across the life-course. Using a rat model for exposure to an endocrine disrupting chemical (EDC), we show that early-life chemical exposure causes metabolic dysfunction in adulthood and reprograms histone marks in the developing liver to accelerate acquisition of an adult epigenomic signature. This epigenomic reprogramming persists long after the initial exposure, but many reprogrammed genes remain transcriptionally silent with their impact on metabolism not revealed until a later life exposure to a Western-style diet. Diet-dependent metabolic disruption was largely driven by reprogramming of the Early Growth Response 1 (EGR1) transcriptome and production of metabolites in pathways linked to cholesterol, lipid and one-carbon metabolism. These findings demonstrate the importance of epigenome:environment interactions, which early in life accelerate epigenomic aging, and later in adulthood unlock metabolically restricted epigenetic reprogramming to drive metabolic dysfunction