Morphing attack detection has become an essential component of face recognition systems for ensuring a reliable verification scenario. In this paper, we present a multimodal learning approach that can provide a textual description of morphing attack detection. We first show that zero-shot evaluation of the proposed framework using Contrastive Language-Image Pretraining (CLIP) can yield not only generalizable morphing attack detection, but also predict the most relevant text snippet. We present an extensive analysis of ten different textual prompts that include both short and long textual prompts. These prompts are engineered by considering the human understandable textual snippet. Extensive experiments were performed on a face morphing dataset that was developed using a publicly available face biometric dataset. We present an evaluation of SOT A pre-trained neural networks together with the proposed framework in the zero-shot evaluation of five different morphing generation techniques that are captured in three different mediums.

Face Recognition Systems (FRS) are increasingly vulnerable to face-morphing attacks, prompting the development of Morphing Attack Detection (MAD) algorithms. However, a key challenge in MAD lies in its limited generalizability to unseen data and its lack of explainability-critical for practical application environments such as enrolment stations and automated border control systems. Recognizing that most existing MAD algorithms rely on supervised learning paradigms, this work explores a novel approach to MAD using zero-shot learning leveraged on Large Language Models (LLMs). We propose two types of zero-shot MAD algorithms: one leveraging general vision models and the other utilizing multimodal LLMs. For general vision models, we address the MAD task by computing the mean support embedding of an independent support set without using morphed images. For the LLM-based approach, we employ the state-of-the-art GPT-4 Turbo API with carefully crafted prompts. To evaluate the feasibility of zero-shot MAD and the effectiveness of the proposed methods, we constructed a print-scan morph dataset featuring various unseen morphing algorithms, simulating challenging real-world application scenarios. Experimental results demonstrated notable detection accuracy, validating the applicability of zero-shot learning for MAD tasks. Additionally, our investigation into LLM-based MAD revealed that multimodal LLMs, such as ChatGPT, exhibit remarkable generalizability to untrained MAD tasks. Furthermore, they possess a unique ability to provide explanations and guidance, which can enhance transparency and usability for end-users in practical applications.

According to a top consulting report, if the Industry gets it right, linking the physical and digital worlds could generate up to $11.1 trillion a year in economic value by 2025. These have resulted in the exponential growth of the data generated through the IoT devices, which has created a requirement to bring computational power at individual device levels using edge computing rather than sending data to the cloud for analysis. Edge computing can move parts of the service-specific processing and data storage from the central cloud/datacenter to edge network nodes; when combined with Artificial Intelligence (AI), it can bring intelligence at the device level. This help to build a smart/intelligent connected network of edge devices called Edge AI or Edge AIoT (Artificial Intelligence of Things) or Intelligent Internet of Things. To know more about Edge AI please check out our blog on Edge AI: The Era of Distributed AI Computing.

With advanced research happening in the realm of artificial intelligence (AI), the technology is poised to become smarter than its human creators. But until that day, it is like to harbour sexist, racist and even homophobic tendencies – all inherited from its makers' social and cultural biases. This was discussed at some length last year at Rising, one of the country's biggest gatherings of women trailblazers in the fields of data science and AI. Held on March 8 to commemorate Women's Day, the one-day event hosted more than 250 participants and featured more than 15 sessions led by industry leaders, mostly women. One of the speakers on the occasion, Director of Citi Saraswathi Ramachandra, provoked a discussion around a hotly debated topic – Is AI sexist.

According to research announced during the recent Black Hat conference in Vegas, some 62 per cent of infosec pros reckon weaponised AI will be in use by threat actors within 12 months. That artificial intelligence was on the agenda at Black Hat should come as no surprise. The promise of AI, from machine learning through to automation, in cyber security has become a major marketing tool amongst vendors. The good guys are clearly investing heavily in AI-defence research, but what about the bad guys? Itsik Mantin, director of research at Imperva, points to a demonstration at Defcon last week as to how "AI can be used to design malware utilising the results of thousands of hide and seek attempts by malware to sneak past anti-virus solutions by changing itself until it finds the right fit that allows it to sneak below the anti-virus radar."