Abstract Traditional fraud detection approaches often use linking entities, such as device, email, and address, to identify fraudulent transactions and users. However, as fraud methods continue to evolve and escalate, the fraudsters can fabricate the involved entities and thus hide their real intent. To make fraud detection more robust, we incorporate user behaviors in the pipeline and consider biometric characteristics that are difficult to forge. In this work, we conduct a detailed study of how user behavior data can help identify and prevent fraudulent activity in e-commerce. We present Multi-Modal Behavioral Transformer (MMBT), where we combine both inner-page behavioral data, such as mouse trajectory, and inter-page behavioral data, such as page view sequences. We propose to construct mouse trajectory data as an image, treat each mouse position as a pixel in the image, convert the image into small patches, and hence transform the mouse trajectory into patch index sequences. Our experimental results on real-word data show that MMBT significantly outperforms baselines — the precision@recall = 0.1 increases by up to 7%. In addition, we have built an online pipeline to operationalize our model. In production, the 99th percentile latency is maintained below 500 milliseconds, allowing the platform to initiate rapid response measures and prevent potential losses.

Digital audio has been ubiquitous over the past decade. Since it can be easily modified by editing tools, there has been a strong need to protect its content for secure multimedia applications. Existing audio authentication algorithms are mainly focused on either human speech or general audio with music as part of the test data, while special research on music authentication has been somewhat neglected. In this article, we propose a novel algorithm to protect the integrity and authenticity of music signals. Its main contributions include: (1) Music is segmented into beat-based frames, which not only endows the authentication units with more semantic meaning but also perfectly resolves the challenging synchronization problem; (2) Robust hashes are generated from Chroma-based mid-level audio feature which can appropriately characterize the music content, and integrated with an encryption procedure to ensure the security against malicious block-wise vector quantization attack; (3) Fuzzy logic is adopted to make the authentication decision in light of three measures defined on bit errors, coinciding with the inherent blurred nature of authentication. Experiments exhibit good discriminative ability between admissible and malicious operations.

Time-scale modification and pitching shifting are two recognized challenging attacks to music copyright protection. To resist them simultaneously, a novel robust hashing method is proposed by combining the strength of music beat segmentation and chroma-based music feature. These two measures are aimed at solving the problem of desynchronization and frequency shifting respectively. Moreover, two layers of scrambling are performed to ensure the security. Experiments exhibit remarkable robustness against various attacks including pitch [email protected]%, time-scale [email protected]%, and [email protected]/10 etc.

A novel audio fingerprinting method that is highly robust to Time Scale Modification (TSM) and pitch shifting is proposed. Instead of simply employing spectral or tempo-related features, our system is based on computer-vision techniques. We transform each 1-D audio signal into a 2-D image and treat TSM and pitch shifting of the audio signal as stretch and translation of the corresponding image. Robust local descriptors are extracted from the image and matched against those of the reference audio signals. Experimental results show that our system is highly robust to various audio distortions, including the challenging TSM and pitch shifting.