Abstract

In the present era, the increasing number of network devices and ubiquitous computing leads to the flow of enormous amounts of data traffic, including sensitive and confidential information, on the internet and carrying out commercial and banking transactions online. This gives cybercriminals an opportunity to launch an attack like phishing to steal confidential information from the user and gain unauthorized access. This can be mitigated by the help of an intelligent machine learningbased phishing detection system, which can detect potential phishing attacks and take appropriate action. In this paper, we have addressed this major cyber issue and proposed a machine learning-based phishing detection scheme (in short, EM-PAD), which is trained on a benchmark dataset and evaluated on standard metrics: F1-score and Accuracy. The proposed model is compared with different existing schemes based on Accuracy, indicating that it has outperformed them with remarkable results

Abstract

In the realm of smart city surveillance, consumer electronics (CE) devices communicate through vulnerable wireless channels, posing significant security risks. To ensure secure and uninterrupted services for residents, continuous monitoring is imperative. Existing authentication methods for CE in smart cities authenticate users/devices at the onset of a communication session, assuming they remain authenticated throughout. However, temporary access by an attacker to the user's or CE device can lead to impersonation. To mitigate this security threat, we propose a continuous authentication (CA) protocol utilizing vector similarity search (VSS), which has emerged as a novel approach to enhance security and privacy in CE deployed within smart city surveillance systems. Through the employment of VSS techniques and secure communication via session key establishment, the proposed scheme continuously monitors user behavior and verifies legitimacy, ensuring seamless operation and protection against a myriad of potential threats. This innovative framework enhances the resilience of smart city infrastructures against unauthorized access, data tampering, data poisoning attacks, and other malicious activities.

Abstract

In facial recognition systems, quality extends beyond conventional perceptual quality to features incorporating identity information. Most facial image datasets embark on factors such as illumination and pose, making current systems robust enough to these factors with impressive recognition performance. Still, it is imperative to acknowledge that age variation and emotional similarity significantly influence identity. Variations in these features might significantly deceive the FR systems. These features also serve as easy channels for adversarial attacks on FR systems that alter facial features, such as morphing. Hence, making FR systems sensitive to the variations introduced over the range of these features is critical. We propose that the Unified Tri-Feature Quality Metric (U3FQ) be incorporated. This novel assessment framework integrates three critical elements: age variance, facial expression similarity, and congruence scores from state-of-the-art recognition models such as VGG-Face, ArcFace, FaceNet, and OpenFace. The weighting U3FQ utilizes an advanced learning paradigm, employing a Regression Network model for facial image quality assessment. U3FQ was rigorously evaluated against general IQA techniques—BRISQUE, BLINDS-II, RankIQA, and specialized FIQA methodologies like PFE, SER-FIQA, and SDD-FIQA. Results are backed up with qualitative analysis on the effectiveness of the generated quality scores through DET plots of FNMR on different age ranges, expression matches heat maps, and Expected Verification Rate (EVRC) curves on various datasets.

Abstract

The difficulty of reliably extracting characters had delayed the character recognition solutions (or OCRs) in Indian languages. Contemporary research in Indian language text recognition has shifted towards recognizing text in word or line images without requiring sub-word segmentation, leveraging Connectionist Temporal Classification (CTC) for modeling unsegmented sequences. The next challenge is the lack of public data for all these languages. And there is an immediate need to lower the entry barrier for startups or solution providers. With this in mind, (i) we introduce Mozhi dataset, a novel public dataset comprising over 1.2 million annotated word images (equivalent to approximately 120 thousand text line images) across 13 languages. (ii) We conduct a comprehensive empirical analysis of various neural network models employing CTC across 13 Indian languages. (iii) We also provide APIs for our OCR models and web-based applications that integrate these APIs to digitize Indic printed documents. We compare our model’s performance with popular publicly available OCR tools for end-to-end document image recognition. Our model outperform these OCR engines on 8 out of 13 languages.

Abstract

This paper presents a diverse compilation of Indic offline handwritten documents. Our dataset comprises 91K handwritten document images captured through unconstrained camera across thirteen Indic languages: Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Manipuri, Marathi, Oriya, Punjabi, Tamil, Telugu, and Urdu, contributed by 1,220 writers. This dataset encompasses 2600K words and includes 566,187 unique words featuring diverse content types, such as alphabetic and numeric. Additionally, we establish a high baseline for the proposed dataset, facilitating evaluation, benchmarking and explicitly focusing on word recognition tasks. Our findings indicate that our dataset is an effective training source for enhancing performance on respective datasets.

Abstract

The recent surge in the vehicle market has led to an alarming increase in road accidents. This underscores the critical importance of enhancing road safety measures, particularly for vulnerable road users like motorcyclists. Hence, we introduce the rider intention prediction (RIP) competition that aims to address challenges in rider safety by proactively predicting maneuvers before they occur, thereby strengthening rider safety. This capability enables the riders to react to the potential incorrect maneuvers flagged by advanced driver assistance systems (ADAS). We collect a new dataset, namely, rider action anticipation dataset (RAAD) for the competition consisting of two tasks: single-view RIP and multi-view RIP. The dataset incorporates a spectrum of traffic conditions and challenging navigational maneuvers on roads with varying lighting conditions. For the competition, we received seventy-five registrations and five team submissions for inference of which we compared the methods of the top three performing teams on both the RIP tasks: one state-space model (Mamba2) and two learning-based approaches (SVM and CNN-LSTM). The results indicate that the state-space model outperformed the other methods across the entire dataset, providing a balanced performance across maneuver classes. The SVM-based RIP method showed the second-best performance when using random sampling and SMOTE. However, the CNN-LSTM method underperformed, primarily due to class imbalance issues, particularly struggling with minority classes. This paper details the proposed RAAD dataset and provides a summary of the submissions for the RIP 2024 competition.

Abstract

The problem of determining geolocation through visual inputs, known as Visual Place Recognition (VPR), has attracted significant attention in recent years owing to its potential applications in autonomous self-driving systems. The rising interest in these applications poses unique challenges, particularly the necessity for datasets encompassing unstructured environmental conditions to facilitate the development of robust VPR methods. In this paper, we address the VPR challenges by proposing an Indian driving VPR dataset that caters to the semantic diversity of unstructured driving environments like occlusions due to dynamic environments, variations in traffic density, viewpoint variability, and variability in lighting conditions. In unstructured driving environments, GPS signals are unreliable often affecting the vehicle to accurately determine location. To address this challenge, we develop an interactive image-to-image tagging annotation tool to annotate large datasets with ground truth annotations for VPR training. Evaluation of the state-of-the-art methods on our dataset shows a significant performance drop of up to 15%, defeating a large number of standard VPR datasets. We also provide an exhaustive quantitative and qualitative experimental analysis of frontal-view, multi-view, and sequence-matching methods. We believe that our dataset will open new challenges for the VPR research community to build robust models.

Abstract

With the increased importance of autonomous navigation systems has come an increasing need to protect the safety of Vulnerable Road Users (VRUs) such as pedestrians. Predicting pedestrian intent is one such challenging task, where prior work predicts the binary cross/no-cross intention with a fusion of visual and motion features. However, there has been no effort so far to hedge such predictions with humanunderstandable reasons. We address this issue by introducing a novel problem setting of exploring the intuitive reasoning behind a pedestrian’s intent. In particular, we show that predicting the ‘WHY’ can be very useful in understanding the ‘WHAT’. To this end, we propose a novel, reasonenriched PIE++ dataset consisting of multi-label textual explanations/reasons for pedestrian intent. We also introduce a novel multi-task learning framework called MINDREAD, which leverages a cross-modal representation learning framework for predicting pedestrian intent as well as the reason behind the intent. Our comprehensive experiments show significant improvement of 5.6% and 7% in accuracy and F1-score for the task of intent prediction on the PIE++ dataset using MINDREAD. We also achieved a 4.4% improvement in accuracy on a commonly used JAAD dataset. Extensive evaluation using quantitative/qualitative metrics and user studies shows the effectiveness of our approach.

Abstract

Prior works have addressed the problem of driver intention prediction (DIP) by identifying maneuvers after their onset. On the other hand, early anticipation is equally important in scenarios that demand a preemptive response before a maneuver begins. However, there is no prior work aimed at addressing the problem of driver action anticipation before the onset of the maneuver, limiting the ability of the advanced driver assistance system (ADAS) for early maneuver anticipation. In this work, we introduce Anticipating Driving Maneuvers (ADM), a new task that enables driver action anticipation before the onset of the maneuver. To initiate research in ADM task, we curate Driving Action Anticipation Dataset, DAAD, that is multi-view: in- and out-cabin views in dense and heterogeneous scenarios, and multimodal: egocentric view and gaze information. The dataset captures sequences both before the initiation and during the execution of a maneuver. During dataset collection, we also ensure to capture wide diversity in traffic scenarios, weather and illumination, and driveway conditions. Next, we propose a strong baseline based on a transformer architecture to effectively model multiple views and modalities over longer video lengths. We benchmark the existing DIP methods on DAAD and related datasets. Finally, we perform an ablation study showing the effectiveness of multiple views and modalities in maneuver anticipation

Abstract

This paper presents the competition report on Reading Documents through Aria Glasses (ICDAR 2024 RDTAG) held at the 18th International Conference on Document Analysis and Recognition (ICDAR 2024). From a mixed reality perspective, understanding the text in the world is of paramount importance. However, all day long, always on, machine perception devices like Aria Glasses pose a unique primary challenge of lower resolution due to their power and sensor constraints. Moreover, diverse everyday scenes like variations in the lighting conditions and reading positions further complicate the reading tasks. To address this, we propose a new dataset and a challenge. Specifically, we propose three novel tasks: Isolated Word Recognition in Low Resolution (Task A), Prediction of Reading Order (Task B), and Page Level Recognition (Task C). We provide new training and test sets consisting of document images captured by Aria Glasses while reading diverse documents in English under various everyday scenarios. Our aim is to engage researchers with prior experience in English language OCR, and to establish benchmarks contributing to the academic literature in this field. A total of thirty-three different teams from around the world registered for this competition, and twelve teams submitted their results along with algorithm details. The winning team, SRCB, achieved a 97.23% Character Recognition Rate (CRR) and a 90.45% Word Recognition Rate (WRR) for Task A: Isolated Word Recognition in Low Resolution. Team Gang-of-N won Task B: Prediction of Reading Order with a BLEU score of 0.0939. Team SRCB also won Task C: Page Level Recognition and Reading with a 77.44% average Page Level Character Recognition Rate (PCRR) and a 50.55% average Page Level Word Recognition Rate (PWRR).