Net-Shape-Based Automated Detection of Integrated-Circuit Layout Plagiarism

Plagiarism of integrated-circuit (IC) layout is a problem encountered both in academia and in industry. A procedure was proposed that compares IC layouts based on the physical representation of particular electrical nets, i.e., on the shape of the features drawn on conducting layers (metals and polysilicon). At the heart of this method is the Needleman–Wunsch algorithm, used for decades in tools aligning sequences of amino acids or nucleotides. Here, it is used to quantify the visual similarity of nets within the pair of layouts being compared. The method was implemented in Python and successfully used to identify clusters of similar layouts within two pools of designs: one composed of logic gates and one containing operational transconductance amplifiers.

Detecting malware samples with similar image sets

This paper proposes a method for identifying and visualizing similarity relationships between malware samples based on their embedded graphical assets (such as desktop icons and button skins). We argue that analyzing such relationships has practical merit for a number of reasons. For example, we find that malware desktop icons are often used to trick users into running malware programs, so identifying groups of related malware samples based on these visual features can highlight themes in the social engineering tactics of today’s malware authors. Also, when malware samples share rare images, these image sharing relationships may indicate that the samples were generated or deployed by the same adversaries.To explore and evaluate this malware comparison method, the paper makes two contributions. First, we provide a scalable and intuitive method for computing similarity measurements between malware based on the visual similarity of their sets of images. Second, we give a visualization method that combines a force- directed graph layout with a set visualization technique so as to highlight visual similarity relationships in malware corpora. We evaluate the accuracy of our image set similarity comparison method against a hand curated malware relationship ground truth dataset, finding that our method performs well. We also evaluate our overall concept through a small qualitative study we conducted with three cyber security researchers. Feedback from the researchers confirmed our use cases and suggests that computer network defenders are interested in this capability.

Norm-guided Adaptive Visual Embedding for Zero-Shot Sketch-Based Image Retrieval

Zero-shot sketch-based image retrieval (ZS-SBIR), which aims to retrieve photos with sketches under the zero-shot scenario, has shown extraordinary talents in real-world applications. Most existing methods leverage language models to generate class-prototypes and use them to arrange the locations of all categories in the common space for photos and sketches. Although great progress has been made, few of them consider whether such pre-defined prototypes are necessary for ZS-SBIR, where locations of unseen class samples in the embedding space are actually determined by visual appearance and a visual embedding actually performs better. To this end, we propose a novel Norm-guided Adaptive Visual Embedding (NAVE) model, for adaptively building the common space based on visual similarity instead of language-based pre-defined prototypes. To further enhance the representation quality of unseen classes for both photo and sketch modality, modality norm discrepancy and noisy label regularizer are jointly employed to measure and repair the modality bias of the learned common embedding. Experiments on two challenging datasets demonstrate the superiority of our NAVE over state-of-the-art competitors.