Vulnerability Found in AI Image Recognition

Civil rights commission finds concerning lack of federal oversight of facial recognition

Rapidly evolving facial recognition tools have been increasingly deployed by law enforcement, but there are no federal laws governing its use. Facial recognition technology − which civil rights advocates and some lawmakers have criticized for privacy infringements and inaccuracy − are increasingly used among federal agencies with sparse oversight, the U.S. I was a little discouraged when our engineers sent me these results after hundreds of our spent annotating endometriosis lesions. But then I watch the algorithm recognition videos, and in the end, the results don’t look too bad from a surgical point of view. As you can see, the algorithm recognizes the lesions on the video, but not on all the frames, which is sufficient for practical surgical existence, but also explains why the numerical results are not so good. Nevertheless, almost all lesions are recognized at some point in the video, which is very encouraging.

And I really thought it was interesting that her explanation of how it blew up so fast wasn’t really a technical development as much as an ethical one. Same thing, Portia Woodruff, the woman who was pregnant, taken to jail, charged, even though the woman who they were looking for had committed the crime the month before and was not visibly pregnant, I mean it was so clear they had the wrong person. And yet, she had to hire a lawyer, fight the charges, and she wound up in the hospital after being detained all day because she was so stressed out and dehydrated. CINDY COHN I think, you know, for us in thinking about this, the central issue is who’s in charge of the system and who bears the cost if it’s wrong.

The model is capable of accurately identifying and segmenting overlapping nuclei while maintaining the clarity of the boundaries. 6e,f, where the blue region indicating true-positive segmentation occupies the majority of the area, while the green color indicating false-positive segmentation and the red color indicating false-negative segmentation are fewer compared to other models. This indicates that our model continues to demonstrate high stability and robustness when confronted with complex and variable image scenes. 7 indicates that the RU3S model outperforms the other models in terms of mIoU scores for all label scales.

Security guard shot, killed amid attempted shoplifting at Dallas CVS

Resolution is a bit low for this model, so it works best up close and personal. SimpliSafe’s approach to facial recognition is a little more hands-off and works especially well if you want to spring for a SimpliSafe home monitoring package, which tends to start at around $30 per month. This feature uses SimpliSafe’s outdoor cams and app, allowing you to add face profiles that the cams “ignore” so that monitoring centers only get their Live Guard alerts if the cams see a stranger.

By targeting the alpha channel, the UTSA researchers could disrupt facial recognition systems. And so yeah, when you have people that are relying too heavily on the facial recognition technology and not doing proper investigations, this can have a very harmful effect on, on individual people’s lives. That company told me that they had to pull out of a project in South Africa because they found the technology just did not work on people who had darker skin. But the activist community has brought a lot of attention to this issue that there is this problem with bias and the facial recognition vendors have heard it and they have addressed it by creating more diverse training sets.

In semantic segmentation models, post-processing modules are crucial for optimizing and correcting the initial predictions of the model. This is achieved by eliminating small and isolated regions in the predictions, filling in small voids, or improving the boundary accuracy. These functions are usually performed through morphological operations, such as expansion and erosion, as well as open and close operations to improve the shape and continuity of the prediction results. However, current semi-supervised learning models primarily focus on utilizing both labeled and unlabeled data to improve performance, without giving sufficient consideration to the spatial relationships between pixels. Recent research33 has shown that incorporating spatial relationships between pixels can significantly enhance the accuracy of image semantic understanding. Despite significant progress in the semantic segmentation of pathology images using deep learning, some challenges remain.

The technology can identify patients, match medical records, and secure and audit people’s access to certain areas within a facility. Community Hospital and the company Alcatraz AI implemented an FRT system to enhance security in server rooms where private data and technology are stored. Based on the results of this demonstration test, we will evaluate its practicability and plan to introduce it as an inspection support technology in FY2025.

Semi-supervised learning image segmentation module

Table 2 shows the results of the Pre, Re, Dice and F1 metrics under different methods. It can be seen that our RU3S model achieves excellent performance in the Pre and F1 metrics and compares favorably with the AdvSemiSeg, CAC, DMT and Reco models in all metrics. As for the ST model, although our model is slightly inferior in the DSC, Pre and F1 metrics, it still has an advantage in the Re metric. MIoU can be interpreted as a measure of the degree of similarity between the predicted tumor region and the real tumor region in our tumor segmentation task.

These methods have significantly improved the accuracy and efficiency of segmentation in the field of pathology image analysis. Road bridges are important infrastructure facilities that support our economy and life, but the deterioration of these facilities has become a major social problem. Corrosion of steel materials is one factor causing road bridge deterioration. Corrosion that occurs in the steel material causes the loss of cross-section of the steel material as it progresses, and the durability and load-bearing performance of the equipment gradually deteriorates, which may eventually lead to failure or collapse.

The computer software running the cameras also alerts authorities when it detects a surge in any one section of the festival city, a fire, or if people cross barricades they are not supposed to. The alerts are relayed to personnel on the ground to take corrective action. Blind and low-vision people have experienced remarkable gains in information literacy because of digital technologies, like being able to access an online library offering more than 1.2 million books that can be translated into text-to-speech or digital Braille. JASON KELLEYYeah, I mean, what surprised me is that I think most of us saw that facial recognition sort of exploded really quickly, but it didn’t, actually. And the reason we don’t see that is because we actually have very strong federal and state laws around wiretapping that prevent the collection of this kind of information except in certain circumstances.

Therefore, the facility management needs to understand the thickness of the steel where corrosion occurs. However, with the current inspection method, it is difficult to determine the thickness of steel at the corroded area. Also, when inspecting a large road bridge, costs such as scaffold installation may incur. Against this background, the Ministry of Land, Infrastructure, Transport and Tourism is promoting the introduction of inspection support technology1. To conduct efficient inspection of road bridges, it is a principle to use the technology in the “Inspection support technology performance catalog” specified by the Ministry of Land, Infrastructure, Transport and Tourism for national highways under its direct control.

Reinvent critical workflows and operations by adding AI to maximize experiences, real-time decision-making and business value. In the paper, the UTSA researchers describe the technology gap and offer recommendations to mitigate this type of cyber threat. In testing TSA technologies, similar results were found across self-identified race descriptions. Credential Authentication Technology (CAT-2) facial matching succeeded more than 99 percent of the time for self-identified white volunteers and 98 percent of the time for self-identified Black volunteers. DHS tested eight methods, which are primarily powered by artificial intelligence and used by Customs and Border Protection, the Transportation Security Administration, and Homeland Security Investigations. Elsewhere, underwater drones operating at a depth of up to 100 metres (328.08 ft) send real-time alerts if there is an accident or a visitor slips and goes under while taking a dip.

• Chips under the skin also tend to migrate, and brands can become unreadable over time.
• The Marshals Service has held a contract with facial recognition software company Clearview AI for several years.
• Beyond simple identification, it offers insights into care tips, habitat details, and more, making it a valuable tool for those keen on exploring and understanding the natural world.
• The University of Texas at San Antonio is dedicated to the advancement of knowledge through research and discovery, teaching and learning, community engagement and public service.
• But what was interesting with Clearview is that Venmo actually had this iPhone on their homepage on Venmo.com and they would show real transactions that were happening on the network.

Many organizations are interested in employing deep learning and data science but have a skill and resource gap that impedes adoption of these technologies. To address this need, IBM created an easy deep learning solution specifically for business users. AlphaDog works by leveraging the differences in how AI and humans process image transparency.

Annotation-efficient deep learning for automatic medical image segmentation

Classical self-training frameworks attempt to use all unlabeled images simultaneously, but this approach is problematic. Specifically, different unlabeled images may vary in difficulty, and thus the reliability of the generated pseudo-labels can vary, leading to serious confirmation bias45,46. Pseudo-labels that are incorrect can accumulate during iterations, causing the model to overfit to the wrong supervisory signals. Gene sequencing, medical imaging, and artificial intelligence have significantly enhanced medical diagnosis, allowing for early detection and precise treatment.

• But then I watch the algorithm recognition videos, and in the end, the results don’t look too bad from a surgical point of view.
• They call it I-XRAY and have demonstrated its concerning power to get phone numbers, addresses and even social security numbers in live tests.
• This movement is specifically designed to match the requirements of facial recognition systems.
• At the time the company pointed out that some features advantageous for blind and partially-sighted users of its platforms would lose some benefits, such as the automatic generation of image descriptions.
• Meanwhile, if you’re looking for support on an IBM Power Systems deep learning project, don’t hesitate to contact IBM Systems Lab Services.

It has been demonstrated that the similarity between the pseudo-masks generated during model training can be employed to assess the stability of unlabeled samples. Consequently, mIoU is employed as a metric for gauging the reliability of unlabeled samples and the stability of model training. Furthermore, the utilization of mIoU as a screening criterion offers the additional advantage of enabling the dynamic reflection of the performance changes of the model throughout the training process.

As the model is optimized, changes in the mIoU value can inform the selection strategy for samples, guiding the model towards the handling of increasingly complex data and further improvement in overall performance. Fully supervised semantic segmentation models achieve semantic label assignment at the pixel level by learning from a large number of densely labeled images. However, the main limitation of this model is the difficulty and cost of acquiring high-quality labeled datasets. The labeling process is not only time- and labor-intensive but also often requires specialized knowledge and skills, which may not be feasible in certain scenarios, such as medical image segmentation.

Annual Payment Fraud Intelligence Report: 2024

More recent legislation restricts one-to-many matching using the national facial recognition database. However, individual states have their own databases from public records. The Australian Federal Police reportedly continue to rely on an agency that uses facial recognition provided by the controversial company Clearview AI. For instance, the majority (60%) of survey respondents did not support its use in the workplace for tracking the location of workers.

Our operations span across 80+ countries and regions, allowing us to serve clients in over 190 of them. We serve over 75% of Fortune Global 100 companies, thousands of other enterprise and government clients and millions of consumers. The Federal Trade Commission last year banned Rite Aid from using AI facial recognition technology after finding it subjected customers, especially people of color and women, to unwarranted searches. The FTC said the system based its alerts on low-quality images, resulting in thousands of false matches, and customers were searched or kicked out of stores for crimes they did not commit.

This operation implements an adaptive weighting of the decoder’s input feature map to enhance the model’s attention to critical regions. Facial recognition is rapidly gaining favor as an efficient security feature in airports, cutting down the time and effort it takes to go through security for both passengers and airport personnel. All of the commercial AI-based software used by these airports appears to deliver what they promise. The key is the strategic placement of a wide network of sensors and cameras connected to machine vision software designed to spot anomalies in the footage, which alerts human operators. At Edwards Air Force Base, for example, a system of ground-based radars sweeps over its 308,000 acres. Army Research Laboratory came up with a method using the visible spectrum and existing facial recognition software to generate a visible space from a thermal image.

And honestly, they did have, in the early days, some troubling ideas about how to use facial recognition technology. Hoagland is currently taking a course studying cryptographic hash functions, which are the basis for blockchain technology. This technology is primarily used in the finance sector for digital currencies such as bitcoin. “We are using this technology to provide third-party verification for these cattle photographs taken in Brazil so that they can’t be altered,” explains Hoagland. “This is like a digital currency transaction in a sense.” These photos will be secured so they can’t be manipulated or Photoshopped, providing security for the users of the app. “Using blockchain makes it impossible to say that a photograph was taken somewhere else,” says Hoagland.

The module’s primary function is to focus on significant regions in the image, particularly those that contribute significantly to the classification task, thereby improving the model’s accuracy in recognizing target objects, especially for a few categories43,44. By introducing the Attention module, our model can effectively focus on critical regions of the feature map, improving overall segmentation performance. Finally, a 1×1 convolutional layer is applied to the connected feature maps to combine the features with different void rates. Equation (8) is then used to mix the contextual information at different scales, improving the model’s recognition accuracy. At the intersection of the encoder and decoder, we introduce the Atrous Spatial Pyramid Pooling (ASPP) module as a connecting bridge.

The analysis and interpretation of cytopathological images are crucial in modern medical diagnostics. However, manually locating and identifying relevant cells from the vast amount of image data can be a daunting task. This challenge is particularly pronounced in developing countries where there may be a shortage of medical expertise to handle such tasks. The challenge of acquiring large amounts of high-quality labelled data remains, many researchers have begun to use semi-supervised learning methods to learn from unlabeled data. Although current semi-supervised learning models partially solve the issue of limited labelled data, they are inefficient in exploiting unlabeled samples. To address this, we introduce a new AI-assisted semi-supervised scheme, the Reliable-Unlabeled Semi-Supervised Segmentation (RU3S) model.

[Podcast] A Fresh Approach to Virtual Fencing with Halter

“This method is highly reliable; problem is, we need a lot of data for it,” Riemer says. “We’d either have to wait a very long time until we have photos of all possible fault types, or we’d need to deliberately damage parts.” She adds that manufacturing quality is too high to yield enough images of damage. And it’s at such a high level because even a few errors could have enormous consequences — in the worst case, recalls of entire batches.

Specifically, the model may not accurately detect all cells, indicating that there is still room for improvement in segmentation accuracy. Additionally, while we have reduced our reliance on labeled data, there is still potential for enhancing the utilization of labeled tags. Future research will focus on improving both segmentation accuracy and label utilization efficiency. We will investigate the implementation of advanced deep learning techniques and optimization algorithms, as well as more efficient utilization of unlabeled data. Additionally, we will collaborate closely with clinicians to obtain their feedback and evaluate the model’s performance in real-world medical scenarios. Deep neural networks have led to an increase in the use of image segmentation techniques in the medical field.

Dallas police to use AI facial recognition technology to help catch criminals – FOX 4 News Dallas-Fort Worth

Dallas police to use AI facial recognition technology to help catch criminals.

Posted: Tue, 14 May 2024 07:00:00 GMT [source]

It’s powered by Google’s Imagen 3 Fast image generation model (the same capability is available in Gemini). PowerAI Vision can be used for numerous other applications, such as city traffic management, market customer analysis and X-ray inspection in airports. Deep learning is still relatively young, so it will be exciting to see where else this technology will be applied in the future.

The use of deep learning models for pathology image segmentation often demands substantial computational resources and time, which is a significant obstacle for developing countries with limited resources5. Additionally, obtaining sufficient pathology image data and accurately labeling it can be very challenging in developing countries, primarily due to the scarcity of medical resources. The application of semi-supervised models for pathology image data requires a large amount of unlabeled data and a small amount of labeled data. To achieve satisfactory results, the model must be able to fully utilize the unlabeled data.

Facial Recognition in the Military – Current Applications – Emerj

Facial Recognition in the Military – Current Applications.

Posted: Sun, 10 Nov 2024 03:10:57 GMT [source]

Auto-labeling, in particular, is a good example of the definition of machine learning (”The field of study that gives computers the ability to learn without being explicitly programmed,” according to Arthur Samuel in 1959). If you auto-label a data set, an existing trained model is used to generate labels for images and video frames that have not been manually labeled, which can dramatically shrink the time required for the deep learning process. Therefore, to better utilize the relationships between pixels, we added a CRF post-processing module to our semi-supervised learning model. The CRF module effectively captures the spatial dependencies between pixels and enhances the model’s accuracy in recognizing and segmenting region boundaries. The CRF module enables the model to consider spatial location information between pixels by establishing structured relationships between them. This leads to a better understanding of the global consistency of the image34,35,36.

Combining deep learning and image classification technology, this app scans the content of the dish on your plate, indicating ingredients and computing the total number of calories – all from a single photo! Snap a picture of your meal and get all the nutritional information you need to stay fit and healthy. Accessibility is one of the most exciting areas in image recognition applications. Aipoly is an excellent example of an app designed to help visually impaired and color blind people to recognize the objects or colors they’re pointing to with their smartphone camera. An Attention module was integrated into the decoder section of the ResUNet model.

CNNs work similar to the human brain in that it can extrapolate a picture from a small amount of data by assigning values to certain aspects of an incomplete image and making connections. While the result is not photo-realistic, there are enough key points or landmarks for facial recognition software to make an accurate match in many cases. As much, with the exception of Illinois, which has this really strong law that’s relevant to facial recognition technology. When we have gotten privacy laws at the state level, it says you have the right to get out of the databases.