Inappropriate Image Detection

Inappropriate images can damage your user experience. They can be offensive, violent or violate laws.

Problematic figures are widespread in biomedical journals. A large analysis of 20,621 papers found that about 1 out of 25 contain inappropriately duplicated electrophoretic or microscopic images such as Western blotting.

Turning on communication safety helps protect your child from nudity in photos they receive or try to send. If Messages detects that a photo may contain nudity, it blurs the image and provides helpful resources.

Detection of Duplicate Images

Duplicate images occupy disk space and affect the performance of your PC. You can use duplicate photo finder programs to help you locate identical photos and remove them. These programs can also save you time and effort by scanning for duplicates and removing them automatically.

Inappropriate image duplications in biology papers raise suspicions about the attention given to figure preparation and analysis, and in extreme cases may indicate data fabrication or plagiarism. These problems can be difficult to identify manually, especially when they involve large collections of images.

To assess whether machines can assist human editors in answering Question 16 of the Molecular and Cellular Biology dataset documentation process, we compared the results of two methods for detection inappropriate image duplication. Prompt-tuning based on a dataset of socio-moral values steers CLIP to classify inappropriate content, reducing the labor needed for manual detection. This approach can be applied to other pre-trained models and used to automatically document datasets.

Detection of Misogynistic Images

Misogynistic images are a form of online hate speech and represent a serious challenge for computer systems. Unlike other forms of violence, misogyny is often subtle and disguised as flattering words, jokes, or parody. This makes it difficult to detect automatically.

In order to address this problem, several research groups have developed techniques for detecting inappropriate content. However, these methods are often biased towards certain types of content and tend to over-emphasize sexually explicit images.

To overcome this bias, researchers have developed a system that uses image processing, skin tone detection, and pattern recognition to identify explicit images and categorize them into two possible categories: NSFW and not NSFW. The system also calculates the likelihood of an image to contain explicit content.

To do this, they trained a model on two Arabic tweets datasets and fine-tuned it with misogyny and sarcasm labels. Table 1 shows that the performance of the linear probing model improves; precision and recall remain comparatively low, though.

Detection of Sexual Content

Nudity and sexual content are often found in social media images. As a result, they need to be removed from sites and apps that are intended for a family audience. This can be difficult to do manually, and requires sophisticated image recognition technology.

Many different methods for inappropriate content detection have been developed. These include skin colour analysis, image zoning, random forest and features related to body parts. However, these methods still have room for improvement in order to improve the accuracy of the classification.

The Q16 inappropriateness classifier, which is trained on the SMID dataset, has shown that it performs well when used for detecting NSFW images. It achieves a high rate of accurate identification, and outperforms other pre-trained models such as ResNet50 and ViT-B. This is important, as it will enable on-device machine learning to be deployed in Apple products such as iOS, watchOS and iMessage, helping to keep children safe. This will be particularly useful for families travelling overseas and sending photos to their kids.

Detection of Violence

Detecting violence is an important task in human activity recognition and security systems. Violent behavior is a threat to the safety of people in public places and leads to social instability and unreliability. Therefore, it is necessary to develop a detection system that can identify violent behavior automatically.

The first approach aims to improve the accuracy of violence detection by using scene-based spatiotemporal features and analysing human behaviour. Unlike most image-based methods, which have to deal with a fixed point of view, the human behaviour approach can overcome this limitation by utilising multi-modal information fusion.

For example, the Clarifai explicit content detection API uses five categories (adult, spoof, medical, violence, and racy) to flag images that contain gore, drugs, or suggestive nudity. Other examples include pictures of graphic surgery wounds or genital close-ups, offensive text on products, and offensive symbols such as a swastika or Ku Klux Klan uniform. It is also possible to detect if a person is pointing a gun at a crowd, which may indicate an imminent violent act.

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