Img Tool : herramienta para el análisis forense de imágenes tomadas desde dispositivos móviles
Bibliographic record
Abstract
En la actualidad, el uso de dispositivos moviles ha aumentado considerablemente, debido principalmente a su facilidad de uso, utilidad y la necesidad cada vez mayor entre los usuarios de estar conectados. Debido a esto, no solo ha habido un cambio significativo en la forma de comunicarse, sino que tambien estos dispositivos han sido cada vez mas utilizados en actividades criminales y ataques. La necesidad de la movilidad y la disponibilidad de la informacion en las organizaciones y empresas, ha llevado al aumento del numero de estos dispositivos moviles, y al aumento de numero de incidentes de seguridad (robo de informacion propietaria y la perdida de datos de clientes). La informatica forense es la aplicacion de tecnicas cientificas y analiticas para adquirir, conservar, recopilar y presentar datos (que han sido procesados y almacenados o transmitidos electronicamente por medio de un entorno informatico), que son validos en un proceso legal. El analisis forense realizado en un dispositivo movil, podra ser admitido en la corte, por un juez. En esta aplicacion perteneciente a la informatica forense, se pretende mostrar las acciones fraudulentas que pueden llevarse a cabo en las fotografias tomadas desde dispositivos moviles. Esto es posible gracias a la meta-informacion disponible en la foto que permite que se pueda comprobar si la foto fue tomada desde un dispositivo o no. [ABSTRACT] Nowadays, the use of mobile devices has increased significantly, mainly due to its easy way of use, usefulness and the increasing necessity among users of being connected. From this, there has been a significant change in the way people communicate, but also these devices have being increasingly used in criminal activities and attacks. The need for mobility and availability of information in organizations and companies, which has led to the increasing number of these mobile devices, rocketed the number of security incidents (theft of proprietary information and customer data loss). Computer forensics is the application of scientific and analytical techniques to acquire, preserve, collect and present data( that have been processed and stored or transmitted electronically through a computer environment), which are valid in a legal proceeding. Forensic analysis carried out on a mobile device, may be admitted in court, by a judge. In this application pertaining to computer forensics We pretend to show the fraudulent actions that can be carried out in photographs taken from mobile devices. That’s possible thanks to meta-information available in the photo so We can check if the photo was taken from a device or not.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.085 | 0.023 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".