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@article{210634,
        author = {Sakshi Singh and Dr Ashwani Kumar},
        title = {Beyond Detection: Deep fake Verification, Editorial Accountability and Trustworthy Journalism in the Generative AI Era},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {6},
        pages = {1590-1602},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=210634},
        abstract = {Generative artificial intelligence makes it difficult to use audio visual content as journalistic evidence. However, identifying an item as synthetic does not determine if a news story is true. This conceptual article proposes an Editorial Verification and Accountability Framework (EVAF). The EVAF relates to preservation, provenance assessment, independent corroboration, risk sensitive escalation, publication decisions and post-publication corrections. Using research from journalism studies, information disorder research, media forensics and digital provenance scholarship, the article differentiates between artefact authenticity from source provenance, factual accuracy, and contextual truth. An operational account of how editors can document evidence, reveal remaining uncertainty, and assign decision responsibility was provided, including what editors should do when there are differing views among technical tools, or original files are unavailable. The framework is discussed in relation to multilingual circulation and resource constraints in India and illustrated through a hypothetical newsroom scenario. Six propositions identify potential areas for further testing. EVAF is proposed as a tentative decision support model rather than as a validated detection system or as a declaration that humans cannot make errors.},
        keywords = {deepfakes; journalism; verification; editorial accountability; synthetic media; provenance; multilingual misinformation},
        month = {November},
        }