The AI Info Controversy: Is This Label Undermining Classical Photography? Industry Experts Weigh In

Spotting Deepfakes in an Election Year: How AI Detection Tools Work and Sometimes Fail Global Investigative Journalism Network

Diseases affecting other wheat organs have excellent classification performances; only black rust on the stem presents a slightly lower value. The progress in computer vision and machine learning has created significant opportunities in precision agriculture, namely in the field of livestock management. The incorporation of RGB (Red, Green, Blue) imaging for individual cow identification signifies a point at which technology harmoniously merges with the welfare and efficiency goals of established farming processes. In literature, a tremendous amount of research has been done on identification of cattle by approaching various aspects. This literature review provides a thorough analysis of important studies and significant developments in the field of individual cattle identification systems. Numerous studies have explored various elements of cattle identification, including detection, tracking, identification, and the integration of deep learning and machine learning algorithms.

Educators use it to verify students’ essays, online moderators use it to identify and remove spam content on social media platforms, and journalists use it to verify the authenticity of media and mitigate the spread of fake news. A dataset obtained from Kaggle52 is utilized for training and testing our models, initially comprising 1,924 images for training and 1,932 for testing. However, due to significant overlap between these sets, the test set is discarded, and the training set is utilized exclusively.

How some organizations are combatting the AI deepfakes and misinformation problem

Consequently, a different weight has to be trained specifically for Farm C. The detection result on cattle is presented in Tables 8, 9. This approach leverages the observation that known cattle exhibit consistent predicted IDs across the images, whereas unknowns tend to show more frequent switching between different IDs. By setting a threshold based on analysis of known versus unknown cattle behavior, we effectively filter out individuals do not present in our training data. These unknowns are readily recognizable in the system by their designated labels, “Unknown 1…N.” The proliferation of AI-generated images, including deepfake videos of politicians and other public figures, has led to increased disinformation online.

A Comprehensive Survey of Animal Identification: Exploring Data Sources, AI Advances, Classification Obstacles and the Role of Taxonomy – Wiley Online Library

A Comprehensive Survey of Animal Identification: Exploring Data Sources, AI Advances, Classification Obstacles and the Role of Taxonomy.

Posted: Fri, 11 Oct 2024 07:00:00 GMT [source]

This algorithm can learn, in ways similar to the human brain, what is “normal” and what is “unusual” at the sub-pixel level of images and videos, rather than searching for specific predetermined identifiers of manipulation from the outset. This makes the program adept at both identifying deepfakes from known sources, as well as spotting those created by a previously unknown program. This led to the development of a new metric, the “minimum viewing time” (MVT), which quantifies the difficulty of recognizing an image based on how long a person needs to view it before making a correct identification. After over 200,000 image presentation trials, the team found that existing test sets, including ObjectNet, appeared skewed toward easier, shorter MVT images, with the vast majority of benchmark performance derived from images that are easy for humans. The team speculated that convolutional neural network-based detectors, like its MISLnet algorithm, could be successful against synthetic video because the program is designed to constantly shift its learning as it encounters new examples.

Pros and cons of facial recognition

Out of the 10 AI-generated images we uploaded, it only classified 50 percent as having a very low probability. To the horror of rodent biologists, it gave the infamous rat dick image a low probability of being AI-generated. We tried Hive Moderation’s free demo tool with over 10 different images and got a 90 percent overall success rate, meaning they had a high probability of being AI-generated. However, it failed to detect the AI-qualities of an artificial image of a chipmunk army scaling a rock wall. The move reflects a growing trend among tech companies to address the rise of AI-generated content and provide users with more transparency about how the technology may influence what they see. Despite the challenges, Google’s action addresses the increasing concerns about deepfakes and AI-generated content.

• These capabilities could make Clearview’s technology more attractive but also more problematic.
• GranoScan (GranoScan, 2023) is the first free mobile app dedicated to the in-field detection and recognition of over 80 threats (diseases, pests, weeds, biotic/abiotic damages) affecting wheat.
• We’re working hard to develop classifiers that can help us to automatically detect AI-generated content, even if the content lacks invisible markers.
• These effects seem to confuse the detection tools, making them believe that the photo was less likely to be AI-generated.
• Using AI models trained on large datasets of both real and AI-generated material, they compare a given piece of content against known AI patterns, noting any anomalies and inconsistencies.

This approach outperforms traditional methods and other classifiers such as Adaptive Boosting, K-Nearest Neighbors, and Naive Bayes. Figures 8 and 9 depict the confusion matrix and AUC curves, respectively, further validating the high diagnostic accuracy of both approaches and highlighting the high diagnostic precision and reliability of the suggested model. Furthermore, Approach B exhibited better performance across various datasets, demonstrating its capability to generalize effectively. On the PCOSGen dataset, this approach attained an accuracy of 96.12%, while on the MMOTU dataset, it excelled further, reaching an accuracy of 97.23%.

TikTok still isn’t in the App Store

Openly available AI detection software can be fooled by the very AI techniques they are meant to detect. Despite the examples listed above, facial recognition has the potential to improve different industry sectors when implemented safely, by giving it the appropriate amount of trust. You may revoke this consent at any time with effect for the future, in which case your personal data will be deleted immediately. Otherwise, your data will be deleted if pv magazine has processed your request or the purpose of data storage is fulfilled. “Some of those photos were actually quite bad, so I can’t believe the model did as well as it did with that data,” Picard said. In a 2023 study published in the journal Methods in Ecology and Evolution, Picard and colleagues trained an AI model to classify more than 1,000 insect species.

If the most frequently appearing ID for a given cattle falls below a pre-defined threshold (10), we classify it as Unknown. For the known cattle, the predicted IDs are stable and there are not too many switches while predicted ID for Unknown cattle are switching frequently and max predicted occurrence is lower compared to known cattle. If the percentage of white pixels is lower than a predetermined threshold of 1%, we categorize the cattle as black. Otherwise, we make a prediction for the cattle using the weight of the non-black VGG16-SVM model. By utilizing an adaptive technique, we are able to accurately detect black cattle by dynamically determining grayscale thresholds. 14 represents the sample of determining the cattle into black or non-black cattle.

The future of image recognition

While AI is not commonly used in cancer diagnoses, more and more doctors are deploying it to help them determine what might be cancer, predict what might develop into cancer, and devise personalized treatment plans when cancer is found. By using AI to analyze images — including mammograms, sonograms, x-rays, MRIs, and tissue slides — doctors are getting more precise pictures, along with deeper analyses of what they see. As an example of out-painting, we took a real image from the Israel-Hamas war and used DALL-E 2 to add the extra context of “smoke.” DALL-E 2 also extended the buildings in the image.

According to Android app expert Assemble Debug, future versions of the Google Photos app could soon be able to read more of the supplementary information apps typically embedded in photos. Known as metadata tags, these short pieces of information contain details about the image, often including details of any software used to create or edit them. By uploading an image to Google Images or a reverse image search tool, you can trace the provenance of the image. If the photo shows an ostensibly real news event, “you may be able to determine that it’s fake or that the actual event didn’t happen,” said Mobasher. As you can see, AI detectors are mostly pretty good, but not infallible and shouldn’t be used as the only way to authenticate an image.

We work for WITNESS, an organization that is addressing how transparency in AI production can help mitigate the increasing confusion and lack of trust in the information environment. However, disclosure techniques such as visible and invisible watermarking, digital fingerprinting, labelling, and embedded metadata still need more refinement to address at least issues with their resilience, interoperability, and adoption. WITNESS has also done extensive work about the socio-technical aspects of provenance and authenticity approaches that can help people identify real content. Instagram, a Meta company, has recognized that “young people can lie about their date of birth” since its March 2021 report. Since then, Instagram has been developing and deploying “artificial and machine learning technology” to identify those users who falsely represent themselves as older—and younger—than they really are. The earliest application of this technology was “alert[ing] the [teenage] recipients within their DMs” when they are contacted by adults interacting disproportionately with users under 18 years old.

The Food and Drug Administration has approved AI-assisted tools to help detect cancers of the brain, breast, lung, prostate, skin, and thyroid. Detection tools calibrated to spot synthetic media crafted with GAN technology might not perform as well when faced with content generated or altered by diffusion models. In our testing, the plugin seemed to perform well in identifying results from GAN, likely due to predictable facial features like eyes consistently located at the center of the image.

During this tracking phase, detected cattle are tracked and assigned a unique local identifier, such as 1, 2… N. Additionally, it is beneficial for counting livestock, particularly cattle. Cattle tracking in this system was used for two stages, the same as the detection stage, data collection for training, and improving the identification process. For data collection, the detected cattle were labeled by locally generated ID. Locally labeled detected cattle were categorized into individual folders followed by their local ID as shown in Fig.

However, it’s up to the creators to attach the Content Credentials to an image. Start by asking yourself about the source of the image in question and the context in which it appears. None of the above methods will be all that useful if you don’t first pause while consuming media — particularly social media — to wonder if what you’re seeing is AI-generated in the first place.

The task was designed this way since farmers represent a category of practitioners who prefer peer-to-peer learning and are experiential learners (Sewell et al., 2017). We followed and embraced this co-design approach because it is crucial to design new technologies jointly with farmers in a participatory manner rather than imposing them and expecting end users to adopt and adapt (Kenny and Regan, 2021). The deep learning model runs on a dedicated server that is not reachable by the mobile app directly. Interactions between the mobile app and deep learning server are managed by AgroSat APIs that receive data and requests by the mobile app, apply pre-processing activities, send data to the deep learning server, wait for results, store and send them back to GranoScan. Figure 2 shows the internal architecture of the proposed solution, highlighting data flows among the GranoScan mobile app, AgroSat server and AI server. Monitoring the health of dairy animals is also essential in dairy production.

• Future research should incorporate multi-source datasets to enhance model robustness.
• While more holistic responses to the threats of synthetic media are addressed across the information pipeline, it is essential for those working on verification to stay abreast of both generation and detection techniques.
• The results highlight the potential of the proposed framework to streamline the diagnostic process, reducing manual errors and time consumption while facilitating timely interventions for women’s reproductive health.
• Right now, 406 Bovine holds a Patent Cooperation Treaty, a multi-nation patent pending in the US on animal facial recognition.
• However, it is essential to note that detection tools should not be considered a one-stop solution and must be used with caution.

Researchers and nonprofit journalism groups can test the image detection classifier by applying it to OpenAI’s research access platform. I do a lot of special effects work that involves heavy use of Photoshop’s AI tools and none of my images have this tag. It makes me wonder if the Instagram is still “testing” this feature and only a small number of people can even see it at this point. As a professional underwater photographer and composite artist whose work is often assumed to be AI generated, I appreciate the labels at times.