L’entreprise analyse des photographies de scènes de crime issues d’Europe - V

USPA NEWS - L’apprentissage de la photographie ne se limite pas à la maîtrise des fonctions techniques de l’appareil. Il exige surtout la compréhension et l’appréciation de l’art photographique. Nous concentrons cette étude sur le rôle et l’importance des images de criminalité et des images sélectionnées dans les médias.

Dans les médias, l’image oriente l’attention du public vers la question visée. La photographie constitue donc un levier central pour capter, diriger et maintenir l’intérêt. Elle peut émouvoir, informer et inspirer chaque individu. Nous recommandons d’intégrer des images pertinentes pour renforcer la clarté du message et l’impact éditorial.

Les médias et la société sont interdépendants. Sans médias, la société perd l’accès rapide aux informations récentes. Sans société, les médias perdent leur raison d’être et disparaissent. Le public fait des supports d’information ses principales sources d’actualités [1–7].

VI. PISTES D’AMÉLIORATION
Nous recommandons les actions suivantes:
- Sélectionner les images selon l’objectif d’information et de sensibilisation, non selon le seul potentiel commercial.
- Limiter la publication d’images extrêmes pour protéger la dignité des victimes et éviter des traumatismes chez le public.
- Former les audiences aux critères d’évaluation d’une image de crime pour améliorer la réception du message.
- Garantir une qualité d’impression élevée; recueillir et intégrer les retours publics pour améliorer la qualité.
- Adapter les images aux besoins des audiences (couleur, format, angle).
- Mesurer régulièrement la satisfaction du public afin d’identifier forces et faiblesses.
- Combiner un texte pertinent avec des images de qualité pour optimiser la transmission.
- Défendre la véracité et l’impartialité; publier sans intention malveillante.
- Éduquer le public au rôle des images dans l’information criminelle.
- Inviter chaque citoyen à analyser attentivement le message afin d’atteindre l’objectif d’alerte et de prévention.

Références

[1] L. Zhijun and W. Ning, “A Cyber Crime Investigation Model Based on Case Characteristics,” 2017 4th International Conference on Information Science and Control Engineering (ICISCE), 2017, pp. 11–15, doi: 10.1109/ICISCE.2017.12.

[2] F. Ahmed, F. Khelifi, A. Lawgaly and A. Bouridane, “The ‘Northumbria Temporal Image Forensics’ Database: Description and Analysis,” 2020 7th International Conference on Control, Decision and Information Technologies (CoDIT), 2020, pp. 982–987, doi: 10.1109/CoDIT49905.2020.9263888.

[3] N. Nayak, P. N. Hegde, Anusha, P. Nayak, P. S. Venugopala and T. Kumaki, “Morphological Pattern Spectrum Based Image Manipulation Detection,” 2017 IEEE 7th International Advance Computing Conference (IACC), 2017, pp. 596–599, doi: 10.1109/IACC.2017.0127..

[4] K. Kageyama, T. Kumaki and T. Koide, “Structuring Element-counting Approach for Morphological Pattern Spectrum-based Image Manipulation Detection,” 2019 2nd International Symposium on Devices, Circuits and Systems (ISDCS), 2019, pp. 1–4, doi: 10.1109/ISDCS.2019.8719260.

[5] Q. Gu, W. Cai, S. Yu and Z. Chen, “An Exploratory Study on Judicial Image Quality Assessment Based on Deep Learning,” 2019 IEEE 19th International Conference on Software Quality, Reliability and Security (QRS), 2019, pp. 300–305, doi: 10.1109/QRS.2019.00046.

[6] D. J. Salim and B. -S. Lin, “Everyone is A Forensic Artist: Sketch-to-Photo Transformation for Human Face,” 2021 IEEE 4th International Conference on Knowledge Innovation and Invention (ICKII), 2021, pp. 118–122, doi: 10.1109/ICKII51822.2021.9574719.

[7] R. Kokila, M. S. Sannidhan and A. Bhandary, “A study and analysis of various techniques to match sketches to Mugshot photos,” 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), 2017, pp. 41–44, doi: 10.1109/ICICCT.2017.7975243.

[8] M. K. J. Kannan, “A bird’s eye view of Cyber Crimes and Free and Open Source Software’s to Detoxify Cyber Crime Attacks - an End User Perspective,” 2017 2nd International Conference on Anti-Cyber Crimes (ICACC), 2017, pp. 232–237, doi: 10.1109/Anti-Cybercrime.2017.7905297.

[9] Y. Cai, D. Li and Y. Wang, “Network Crime Information Retrieval Framework based on Facial Image Recognition,” 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), 2020, pp. 965–969, doi: 10.1109/ICISS49785.2020.9316037.

[10] K. Ravichandran and S. Arulchelvan, “Structural Equation Model Analyzed on Cyber Crime and Media Awareness in India,” 2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM), 2017, pp. 141–146, doi: 10.1109/ICRTCCM.2017.79.

[11] G. Garcia-Zanabria et al., “Mirante: A visualization tool for analyzing urban crimes,” 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 2020, pp. 148–155, doi: 10.1109/SIBGRAPI51738.2020.00028.

[12] K. Biron, W. Mansoor, S. Miniaoui, S. Atalla, H. Mukhtar and K. F. Bin Hashim, “Data Science Tools for Crime Investigation, Archival, and Analysis,” 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2019, pp. 1263–1266, doi: 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00235.

[13] V. Mahor, R. Rawat, S. Telang, B. Garg, D. Mukhopadhyay and P. Palimkar, “Machine Learning based Detection of Cyber Crime Hub Analysis using Twitter Data,” 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON), 2021, pp. 1–5, doi: 10.1109/GUCON50781.2021.9573736.

[14] S. N. Huda Sheikh Abdullah et al., “Assessment of Self-Identity Among Teens Towards Self-Crime Prevention Program,” 2018 Cyber Resilience Conference (CRC), 2018, pp. 1–4, doi: 10.1109/CR.2018.8626870.

[15] P. B. Shailaja Rani and A. Kumar, “Digital Image Forgery Detection Techniques: A Comprehensive Review,” 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), 2019, pp. 959–963, doi: 10.1109/ICECA.2019.8822064.

[16] G. U. Reddy, M. Madhu Bala and B. Padmaja, “An Overview on Digital Forensics Tools used in Crime Investigation for Forgery Detection,” 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA), 2020, pp. 1–5, doi: 10.1109/ICCSEA49143.2020.9132965.

[17] K. M. Mohan, K. Chandra Sekharaiah, P. Premchand, G. U. Pullaiah and B. Malathi, “Approving Psycho-Neuro-Computer Systems to prevent (Systemic Vs Individualistic Perspective) Cybercrimes in Information Highway,” 2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS), 2018, pp. 205–209, doi: 10.1109/CCCS.2018.8586801.

[18] R. Dremliuga, A. Iakovenko and N. Prisekina, “Crime in virtual reality: discussion,” 2019 International Conference on Cybersecurity (ICoCSec), 2019, pp. 81–85, doi: 10.1109/ICoCSec47621.2019.8970947.

[19] G. Rusman and E. Popova, “Development of the Software for Examination of the Crime Scene by Using Virtual Reality, Based on Spherical Panoramic Shot and 3D-Scanning,” 2020 Global Smart Industry Conference (GloSIC), 2020, pp. 297–302, doi: 10.1109/GloSIC50886.2020.9267871.

[20] D. S. Hartley, “Modeling psycho-social attributes in conflict, extended,” Winter Simulation Conference Proceedings, 1995., 1995, pp. 1244–1249, doi: 10.1109/WSC.1995.479031.

[21] N. Jayakanthan and J. Wisvambari, “An Investigation based Approach to Detect the Root cause of Crime in Modern Era,” 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), 2021, pp. 1–4, doi: 10.1109/ICAECA52838.2021.9675704.

[22] R. Ramirez and N. Choucri, “Improving Interdisciplinary Communication With Standardized Cyber Security Terminology: A Literature Review,” in IEEE Access, vol. 4, pp. 2216–2243, 2016, doi: 10.1109/ACCESS.2016.2544381.

[23] U. Merlone, E. Manassero and G. Zara, “The lingering effects of past crimes over future criminal careers,” 2016 Winter Simulation Conference (WSC), 2016, pp. 3532–3543, doi: 10.1109/WSC.2016.7822382.

[24] D. Ackerman and H. Mehrpouyan, “Modeling human behavior to anticipate insider attacks via System Dynamics,” 2016 Symposium on Theory of Modeling and Simulation (TMS-DEVS), 2016, pp. 1–6, doi: 10.23919/TMS.2016.7918809.

[25] Jian Li, S. G. Nikolov, C. P. Benton and N. E. Scott-Samuel, “Adaptive summarisation of surveillance video sequences,” 2007 IEEE Conference on Advanced Video and Signal Based Surveillance, 2007, pp. 546–551, doi: 10.1109/AVSS.2007.4425369.

[26] A. Jevremovic et al., “Keeping Children Safe Online With Limited Resources: Analyzing What is Seen and Heard,” in IEEE Access, vol. 9, pp. 132723–132732, 2021, doi: 10.1109/ACCESS.2021.3114389.

[27] C. H. Ngejane, G. Mabuza-Hocquet, J. H. P. Eloff and S. Lefophane, “Mitigating Online Sexual Grooming Cybercrime on Social Media Using Machine Learning: A Desktop Survey,” 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD), 2018, pp. 1–6, doi: 10.1109/ICABCD.2018.8465413.

[28] H. Ghodosi and I. Lee, “Unconditional security and privacy preserving oblivious transfer,” 2012 7th International Conference on Computing and Convergence Technology (ICCCT), 2012, pp. 1042–1047.