Abstract—Images are the things those are being used in day to day life so much that it has become an important aspect in everyone’s life. Images have always been a popular thing to be instantly shared among each other around the globe. Today’s world is a place where one can get easily influenced and images being perfect to influence as it easily conveys a lot using visual representation also it tends to be remembered more. Images can have positive influence via means of motivational quotes images also it can have negative influence via means of sharing nude images, images of killing etc, retrieval of images with object-of-interest from vast pool of social media images has been a research interest in understanding human emotions, situations leading to conflicts, in field of cybercrime etc. The proposed system is a model for suspicious image detection; it is a system which will decide the image is a good or bad influence based on the past and present experiences.
The proposed system makes use of more number of attributes than the existing system which makes prediction more accurate. It is a machine learning application where a large amount of data is analyzed and some meaningful information is extracted. The system makes use of the huge dataset of data given to it and that data being trained.Keywords—social media; image retrieval; positive-negative influence;INTRODUCTIONIn the recent years, Social media has been a place where people from different parts and corner around the world come and interact with each other. The ever increasing popularity of social media has impacted people’s life in many ways be it about making their brand presence value felt, be it about knowing people and staying connected with them from wherever you are etc. The most famous social media sites are Facebook, Twitter, Instagram. All three top social media sites have one thing in common i.
e. “Images” constitute large amount of their data shared cluster. Using social media one can propagate information as well as their ideas and opinions. The above stated aspect can turn out to be a positive influence on people as well as it can lead to being a negative influence on people as well. Removing such negative influence content has always been a pain task because the current system does not use any sophisticated techniques to find such content instead it relies on human to do this task which leads to use of additional human resources resulting in increasing cost and time overhead to do that task. Let us look at instances where certain terrorist organization propagate their ideas via form of images. However due to large number of user accounts and social groups on social media sites, it has become increasingly difficult to manually identify and track such groups since not all users will be into spreading negative influences. We are currently focusing on images that contains Objects-of-interest such as ISIS images (Fig.
1 (a)), Riots Images (Fig.1 (b)). As seen from Fig.1, Different account share image varying containing similar content but with different editing styles on social media sites, making it difficult for object recognition and Pre-processing.
Fig 1.1. ISIS images Fig. 1.2. Riots images In computer vision, object detection is addressed as one of the most challenging problems as it is prone to localization and classification error 3.
A human brain makes vision seems easy. It doesn’t take any effort for humans to tell apart a lion and a jaguar, read a sign, or recognizes a human’s face. But these are actually hard problems to solve with a computer: they only seem easy because brains are incredibly good at understanding images. In the last few years the field of machine learning has made a tremendous progress on addressing these difficult problems. In particular, found a kind of model called a deep convolutional neural network can achieve reasonable performance on hard visual recognition task than those old SIFT such that it matching exceeds human performance in some domains 1 13.
Researches have demonstrated steady progress in computer vision by validating their work against ImageNet – an academic benchmark for computer vision 8. Successive models continue to show improvements, each time achieving a new state-of-the-art result: QuocNet, AlexNet, Inception (GoogLeNet), BN-Inception-v2. Researchers both internal and external to Google have published papers describing all these models but the results are still hard to reproduce. *****Now taking the next step by releasing code for running image recognition on latest model, Inception-v3.*** Inception-v3 is trained for the ImageNet Large Visual Recognition using the data from 2012. This is a standard task in computer vision, where models try to classify entire images into 1000 classes like “Zebra”, “Dalmatian”, and “Dishwasher”. For example, here are the results from AlexNet classifying some images in following. Fig 1.
3. Machine learning technique Our proposed model provides a model using which we can extract the region of interest (ROI) from a particular image resulting in negative influence image being easily found as compared to the previous techniques present. ROI can extract region from which it will further increase chance of detecting suspicious image being uploaded. Our contributions are in the following aspects: 1) reduced network for retrieving object of interest from social platform which reduces the amount of data required for the purpose of training and computation time.
2) framework for detection of explicit images as well as suspicious images being uploaded on social platform