Day: March 27, 2024

Capt SB TyagiChief Councillor of ICISSM As Warren Buffet said, “It takes 20 years to build a reputation and five minutes to ruin it. If you think about that, you’ll do things differently.” A single act of crime on your property could diminish your brand. Hotels need to partner with an experienced physical security provider, and ensure that the entire staff understands the need to keep security top of mind – always. Savvy hoteliers should consider the following solutions when looking to improve their property’s security. Meet and Greet One of simplest, but most effective, ways of securing a property is to provide excellent customer service. “Engage customers you encounter,” Clifton said, “Ask them about their stay and if there’s anything you can do to help. You don’t have to throw more labour at security. Just make employees a little smarter.” Meet and greet is the policy and program which efficient hotels deploy now a days and their happy stories depend more and more on its success! It takes management closer and closer to ‘Predictive Profiling’ which seems to be a panacea for many of the evils plaguing security. What is Predictive Profiling? It is a method of situational threat assessment designed to predict and categorize the potential for inappropriate, harmful, criminal and/ or terrorist behaviour that leads to the deployment of procedures and actions necessary to confirm, reduce and/ or eliminate such threats. It is the best practicable methodology for criminal/ terrorist threat mitigation. And the only method that adheres to legal, commercial and civil liberty concerns while still offering an effective security solution. It categorizes threat based on the predicted methods of operation that would be used by a given aggressor to attack a given protected environment. A person holding a box cutter on a train going 100 miles per hour presents a threat in the form of hijacking a train while in transit. However, holding a box cutter in the middle of a train station is not threatening from the point of view of a masscasualty terrorist attack. Whether it involves people or objects, every situation is evaluated according to time, location, and the possible threatening scenarios associated with the specific protected environment. Predictive profiling as a threat assessment technique The Predictive profiling helps predict and categorize potential criminal/ terrorist, their methods of operations based on behaviour, information and situation. It isn’t out of the box thinking but has its moorings in the observation skills of eagle eyed police officers who used to naturally scan people moving in queues and pick up the ones showing palpable signs of nervousness or physical indicators with a fairly good success rate. As a full-fledged art, Predictive Profiling is a focusing technique to spot the telltale signs of a crime/ criminal in the making. Predictive profiling or Pro Active Threat Assessment is a way of surveillance from a criminal/ terrorist’s mind-set. The technique looks for suspicious indicators in the various stages of life cycle of a crime. The crime does not happen overnight and what we fight back or makes headline is just the tip of the iceberg. According to Wikipedia, “Predictive profiling is a method of threat assessment designed to predict and categorize the potential for criminal and/ or terrorist methods of operation based on an observed behaviour, information, a situation and/ or objects.” Predictive profiling offers a unique approach to threat mitigation It begins from the point of view of the aggressor/ adversary and is based on actual adversary’s methods of operation, their modus operandi. This method is applicable to securing virtually any environment and to meeting any set of security requirements. In Predictive Profiling, one uses only the operational profile (not racial or statistical profile) of a terrorist or criminal as the basis for identifying suspicion indicators in a protected environment. When predictively profiling a situation, person or object, one identifies suspicion indicators that correlate with an adversary’s method of operation. For example, if a security officer observes a person walking with an empty suitcase in an airport (the suitcase appears very light; it bounces off the floor) he may identify this suspicious behaviour as an indication of a possible terrorist or criminal method of operation because: Predictive Profiling in Hotel Industry As having been largely successful in aviation security, similar profiling templates are created by the hotel industry. Passport officials scrutinize guests, applicants while on lookout for ubscrupulous elements. Israel, where the concept of Predictive Profiling is said to have originated along with USA, Netherland and host of other countries are pioneering its use. Recall the 26/ 11 Mumbai Attacks and David Colman Headly’s testimony. He did eight hostile resonance of Mumbai, entered and left the country at will, joined an upscale gym and befriended people in the city without raising suspicion. He carried back photos, videos and GPS locations of Taj Hotel, Oberoi Hotel, State Police HQ and Bhabha Atomic Research Centre for his handlers to create a ‘Mock Up’ in Pakistan. He filmed the entire Bhagat Singh Marg (Colaba), Leopold Café and Taj Hotel for hours and yet we failed to get the ‘suspicion’ and prophesy correctly. In our fight to catch up with terrorists, predictive profiling, complimentary to existing security measures is like an intelligent algorithm generating red flags for the outliers and help keep them at bay. Going ahead, we need to fight the war on terror intelligently, reinvent and reposition ourselves on that all-out offensive. Alert to safety issues like never before, major hotels in the country are upping their security quotient in a big way. In fact, the hospitality industry’s apex body, the Hotel Association of India (HAI), with the biggest chains (the Taj, Oberoi and ITC, among others) in the country as members, has arrived at a complete list of security measures. “We had been in continuous discussions and wanted to arrive at recommendations for both big properties that can afford the kind of investment needed and smaller ones which can’t afford some of these hi-tech measures,” said Priya…

Prakash PrabhuChief Business Officer & Co-Founder, VisionBot Computer vision has traditionally been used for inspection in highly structured manufacturing environments. But thanks to increasingly powerful machine learning techniques, and mature data labelling capabilities, Visual AI is becoming capable of monitoring more complex and dynamic processes where humans are involved, this is revolutionizing our approaches to identifying and mitigating failures in a wide range of industrial processes – expanding well beyond traditional automated manufacturing applications and promoting Human Centred AI (HCAI). The oil and gas sector, companies can adopt AI technologies to improve Operations, Safety and Reliability across the production and supply chain. This translates to autonomous processes, improving cost efficiencies, and reducing operational risks. Some of the process impacted by Visual AI include: Intelligent Fire Detection With AI Fire hazards are one of the most severe causes of accidents that may lead to casualties, considerable production loss, and equipment damage. Traditional fire detection was done by human operators through video cameras, especially in petroleum and chemical facilities. However, it’s almost impossible for human operators to spot fires in time with hundreds of video cameras installed in large-scale settings. Human subjectivity, distraction, and visual perception limit the accuracy of human safety supervisors. Intelligent fire detection applies computer vision methods to video cameras to detect fires. Some methods have shown better results when focusing on smoke detections. These use background subtraction to detect motion and reduce computational complexity. The availability of accurate model datasets and improved computation power of edge cameras can now make it possible to undertake complex analysis for Fire and Smoke detection on the camera. Predictive Maintenance and Equipment Failure Detection In oil fields and refineries, deep learning models can be used to detect equipment failures or deterioration. Therefore, custom neural networks are trained to detect anomalies during automated equipment inspection. If an AI model detects a potential issue, an image can be automatically sent to a human supervisor for manual review. By continuously monitoring equipment performance, they can predict potential failures, allowing for proactive maintenance to minimize downtime and prevent accidents. well where deep neural networks (DNN) are applied. Traditional methods of physical interpretation are time-consuming, and the results depend strongly on the human expert (subjectivity). In Industry tests, the ML model’s accuracy was 92% compared to manual interpretation and about 1,000 times faster than the manual method. AI methods can accelerate the process and, even more critically, exclude subjectivity in the interpretation process. Conclusion Today, we are only seeing the beginning of the era of Visual AI-driven applications. Edge AI makes it possible to move AI vision capabilities from the cloud to the field, enabling large-scale and distributed applications. Because of the strategic importance and distinct operational workflows, most oil and gas companies aim to build and operate their Visual AI solutions with a primarily aim to improve maintenance, safety, management, life-cycle sustainability, quality, and operational efficiency. As a leading Visual AI Company, VisionBot helps to leverage the latest in computer vision technology to help businesses and organizations automate processes, improve customer experiences, and gain valuable insights in to their operations. Connect with our experts to understand how companies are using VisionBot™ Visual AI driven Computer Vision to strengthen security, safety and streamline operations. We welcome Technology Integrators and sector speciality VAR’s to become a VisionBot™ channel partner, and discover the opportunity to offer a cutting-edge AI-powered computer vision solution to your customers. *Views expressed in the article are solely of the Author

Weaponized for Malicious Purposes GARIMA GOSWAMYChief Executive Officer and Co-FounderDridhg Security International Pvt. Ltd. On January 7, 2019, Gabon faced an attempted coup sparked by a suspicious video of President Ali Bango, coinciding with rumours of his failing health. Amidst the Russia-Ukraine conflict in February 2022, a deepfake video depicting Volodymyr Zelenskyy surrendering circulated, sowing confusion. Subsequently, in June 2023, hackers aired a fake televised emergency message supposedly from an AI-generated Russian President Vladimir Putin, declaring Martial law due to alleged Ukrainian incursions into Russia. There is also a viral interview of Russian President Vladimir Putin having a conversation with his deepfake, a student from St Petersburg State University. A snapshot of the same is given below: In India, a deepfake video of Bharatiya Janata Party’s Manoj Tiwari speaking multiple languages went viral on WhatsApp before the Delhi elections, resembling David Beckham’s multilingual ‘Malaria no more’ campaign. Initially surfacing in 2018, deepfake technology was exploited by a Reddit user to create objectionable pornographic content featuring celebrities, generating widespread skepticism due to a lack of awareness. More recently, financial frauds leveraging synthetic audio and deepfake videos, often combined with impersonation on video calls, have become prevalent. What began as a seemingly innocuous tool in the entertainment industry has spiraled into a technology facilitating misinformation, defamation, pornography, and financial fraud through social engineering tactics. Good DeepFakes Some uses of deepfake which is not necessarily unethical is the use of text-to-speech for writing audio books for the visually impaired and text-tospeech or speech-to-speech to create synthetic voices, that can be used in the entertainment industry. Then there is the concept of hyper-personalization. Recently, Cadbury used hyper-personalization as an initiative to make the famous Indian actor Shah Rukh Khan a brand ambassador for small businesses. Here videos were created by mixing a deepfake version of the actor along with some shots recorded for the campaign. Now companies like ‘True Fans’ are using hyper-personalization to send customized messages on special occasions at a cost. Understanding Deepfakes Deepfakes represent a form of manipulated media, combining ‘deep learning’ with ‘fake.’ Utilizing artificial intelligence and deep learning techniques, deepfakes involve altering or superimposing content onto existing images, videos, or audio recordings to create convincing yet entirely fabricated scenes. These manipulations often employ auto-encoder convolutional neural networks, which compress input images before reconstruction, or Generative Adversarial Networks (GANs), an unsupervised deep learning algorithm. GANs consist of a generator, which reproduces a specific person’s face, and a discriminator, which identifies flaws in the generated image, leading to iterative improvement until flaws are minimal. Where Lies the Solution? Just as proof of the pudding is in eating it, the detector is already present inside the deepfake creation. Also, some cues can help distinguish the real from the deepfake and some credible software that falls under the category of ‘deepfake detectors.’ It is important to sensitize people about the problem of deepfakes and present credible methods to distinguish the real and the deepfake. For image forensics and authentication of audio and videos, there are some reliable software available. A highly reliable image forensic software, particularly popular with law enforcement agencies in several countries is Amped Authenticate which uses multiple parameters for authentication of images. It conducts file analysis that is used to check the metadata of images. It can also be checked if an image comes from social media. A global analysis involves examining the processing history behind an image. The software through this analysis can demonstrate the presence of resaving, cropping, recapture, and editing. This helps to increase the accuracy of image analysis. Local analysis identifies possible manipulations based on the nature of JPEG file format and the traces left behind by editing software. Using color channels, error level analysis, JPEG ghost maps, and cloned artifacts identification, it is possible to demonstrate areas within an image which has possibly been edited. These are just some of its features. For detection of synthetic voices, a notable tools which comes highly recommended is ResembleAI’s detector. It is user-friendly and a wav or mp3 format file can easily be uploaded. It has two controls which have to be used – frame length and a sensitivity knob. Frame length controls for the granularity of the model’s analysis. By default, the model analyzes the file in 2-second chunks, then provides a real or fake prediction score for each chunk, and ultimately provides a weighted average score for the entire file. The smaller the chunks, the less likely that the chunk will contain both real and fake data, which ultimately improves the accuracy of the model. The sensitivity knob controls the level of scrutiny and aggression the model applies to its file analysis. The higher the sensitivity the more likely that the model will return a false positive result. Increases in sensitivity are meant to capture any instance of synthetic audio in an audio file, and flag the audio as fake. While there are several deepfake detectors which are available for checking the authenticity of videos such as DeepWare, DeepIdentify, Sensity, FakeBuster, FakeBuster by Intel, Kroop AI’s detector, Sensity is recommended as it has a high level of accuracy. It is to be noted that among these names, a distinctive feature of Fakebuster is that it can capture live videos and can be integrated with video conferencing platforms like Zoom and Skype. This made-in-India software is available as open-source and was developed in 2021 keeping in mind the risk of imposters in video conferencing particularly as the frequency of video calls increased after COVID-19. If such software was used, the 2024 Hong Kong fraud involving a finance executive who deposited $25 million to imposters after a fake Zoom call could have been prevented. We can’t only depend on deepfake detectors for this space is evolving regularly and there is a cat-and-mouse chase between deepfake creation software and deepfake detection software. And, no software can guarantee 100% accuracy. It is then important to rely on human intelligence too. Some warning signs and techniques that individuals can use…