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| </head> |
| <body> |
|
|
| |
| <header class="top-header"> |
| <span class="shield">🛡️</span> |
| <h1>Image Forgery Detector |
| <span>Remediation & Enhancement Plan — PGD Student Project</span> |
| </h1> |
| <span class="tag">10 Issues · 4 Severity Levels</span> |
| </header> |
|
|
| |
| <nav class="sidebar"> |
| <div class="sidebar-section">Overview</div> |
| <a href="#summary">Issue Summary</a> |
| <a href="#order">Implementation Order</a> |
| <a href="#done">Definition of Done</a> |
|
|
| <div class="sidebar-section">Critical Fixes</div> |
| <a href="#fix1"><span class="badge critical">C</span> Fix 1 — run_training()</a> |
| <a href="#fix2"><span class="badge critical">C</span> Fix 2 — Real Dataset</a> |
|
|
| <div class="sidebar-section">High Priority</div> |
| <a href="#fix3"><span class="badge high">H</span> Fix 3 — Notebook Order</a> |
| <a href="#fix4"><span class="badge high">H</span> Fix 4 — Eval Metrics</a> |
| <a href="#fix5"><span class="badge high">H</span> Fix 5 — Ablation Study</a> |
|
|
| <div class="sidebar-section">Medium Priority</div> |
| <a href="#fix6"><span class="badge medium">M</span> Fix 6 — train.py Parity</a> |
| <a href="#fix7"><span class="badge medium">M</span> Fix 7 — Project Report</a> |
| <a href="#fix8"><span class="badge medium">M</span> Fix 8 — Literature</a> |
|
|
| <div class="sidebar-section">Low Priority</div> |
| <a href="#fix9"><span class="badge low">L</span> Fix 9 — Grad-CAM</a> |
| <a href="#fix10"><span class="badge low">L</span> Fix 10 — README</a> |
| </nav> |
|
|
| |
| <main class="main"> |
|
|
| |
| <div class="hero"> |
| <h2>Remediation & Enhancement Plan</h2> |
| <p>Every gap found during validation, with step-by-step actions to fix each one. |
| Work through fixes <strong>in priority order</strong> — later fixes depend on earlier ones.</p> |
| <div class="stats"> |
| <div class="stat c"><div class="n">2</div><div class="l">Critical</div></div> |
| <div class="stat h"><div class="n">3</div><div class="l">High</div></div> |
| <div class="stat m"><div class="n">3</div><div class="l">Medium</div></div> |
| <div class="stat lo"><div class="n">2</div><div class="l">Low</div></div> |
| </div> |
| </div> |
|
|
| |
| <section id="summary"> |
| <h2 style="font-size:20px;font-weight:800;margin-bottom:16px;color:#0f172a;">Quick Reference: All Issues</h2> |
| <table class="summary-table"> |
| <thead> |
| <tr> |
| <th>#</th><th>Severity</th><th>Issue</th><th>Files Affected</th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr> |
| <td><span class="num-badge">1</span></td> |
| <td><span class="badge critical">CRITICAL</span></td> |
| <td><code>run_training()</code> never defined in notebook</td> |
| <td><code>.ipynb</code></td> |
| </tr> |
| <tr> |
| <td><span class="num-badge">2</span></td> |
| <td><span class="badge critical">CRITICAL</span></td> |
| <td>Synthetic toy data — model learns nothing real</td> |
| <td><code>.ipynb</code>, <code>train.py</code></td> |
| </tr> |
| <tr> |
| <td><span class="num-badge">3</span></td> |
| <td><span class="badge high">HIGH</span></td> |
| <td>Notebook section order is broken (§9 → §7 → §8)</td> |
| <td><code>.ipynb</code></td> |
| </tr> |
| <tr> |
| <td><span class="num-badge">4</span></td> |
| <td><span class="badge high">HIGH</span></td> |
| <td>No meaningful evaluation metrics (only accuracy)</td> |
| <td><code>.ipynb</code></td> |
| </tr> |
| <tr> |
| <td><span class="num-badge">5</span></td> |
| <td><span class="badge high">HIGH</span></td> |
| <td>Ablation study conclusion is invalid (all 100%)</td> |
| <td><code>.ipynb</code></td> |
| </tr> |
| <tr> |
| <td><span class="num-badge">6</span></td> |
| <td><span class="badge medium">MEDIUM</span></td> |
| <td><code>train.py</code> only trains M3, not M1/M2</td> |
| <td><code>train.py</code></td> |
| </tr> |
| <tr> |
| <td><span class="num-badge">7</span></td> |
| <td><span class="badge medium">MEDIUM</span></td> |
| <td>Project report document missing from repo</td> |
| <td><code>Documents/</code></td> |
| </tr> |
| <tr> |
| <td><span class="num-badge">8</span></td> |
| <td><span class="badge medium">MEDIUM</span></td> |
| <td>No literature comparison or baseline results</td> |
| <td><code>.ipynb</code></td> |
| </tr> |
| <tr> |
| <td><span class="num-badge">9</span></td> |
| <td><span class="badge low">LOW</span></td> |
| <td><code>get_gradcam()</code> in app.py is simplified vs notebook</td> |
| <td><code>app.py</code></td> |
| </tr> |
| <tr> |
| <td><span class="num-badge">10</span></td> |
| <td><span class="badge low">LOW</span></td> |
| <td>Gradio vs Streamlit discrepancy not documented</td> |
| <td><code>README.md</code></td> |
| </tr> |
| </tbody> |
| </table> |
| </section> |
|
|
| |
| <div class="section-header"> |
| <span class="pill critical">Critical Fixes</span> |
| <hr> |
| </div> |
|
|
| |
| <section id="fix1"> |
| <div class="fix-card critical"> |
| <div class="fix-header"> |
| <div class="fix-num">1</div> |
| <div class="fix-title">Add the Missing <code>run_training()</code> Function to the Notebook</div> |
| </div> |
| <div class="fix-body"> |
| <div class="problem-box"> |
| <div class="label">Problem</div> |
| Cell 16 calls <code>run_training('M1', ...)</code>, <code>run_training('M2', ...)</code>, and <code>run_training('M3', ...)</code> |
| but this function is <strong>never defined</strong> in any visible cell. The notebook cannot be run |
| end-to-end — an examiner will get a <code>NameError</code> immediately. |
| </div> |
| <div class="why-box"> |
| <strong>Why it matters:</strong> A Colab notebook is expected to be fully self-contained. |
| Every function called must be defined above its call site. |
| </div> |
|
|
| <div class="steps"> |
| <div class="step"><p>Open <code>Image_Forgery_Detection_Colab_1.ipynb</code> in Google Colab.</p></div> |
| <div class="step"><p>Insert a new code cell <strong>between the <code>build_model()</code> cell and the ablation execution cell</strong> (between current cell-9 and cell-16).</p></div> |
| <div class="step"><p>Paste the following function into that new cell:</p></div> |
| </div> |
|
|
| <div class="code-wrap"> |
| <div class="code-label">Python — run_training() definition</div> |
| <pre><span class="kw">def</span> <span class="fn">run_training</span>(model_type, train_ds_base, val_ds_base, n_train, n_val): |
| <span class="fn">print</span>(<span class="st">f"\n{'='*55}"</span>) |
| <span class="fn">print</span>(<span class="st">f"Training {model_type}"</span>) |
| <span class="fn">print</span>(<span class="st">f"{'='*55}"</span>) |
|
|
| train_ds = <span class="fn">adapt_dataset_for_model</span>(train_ds_base, model_type) |
| val_ds = <span class="fn">adapt_dataset_for_model</span>(val_ds_base, model_type) |
|
|
| model = <span class="fn">build_model</span>(model_type) |
| model.<span class="fn">compile</span>( |
| optimizer=<span class="st">'adam'</span>, |
| loss=<span class="st">'binary_crossentropy'</span>, |
| metrics=[<span class="st">'accuracy'</span>] |
| ) |
|
|
| steps_per_epoch = <span class="fn">max</span>(<span class="nb">1</span>, <span class="fn">int</span>(np.<span class="fn">ceil</span>(n_train / BATCH_SIZE))) |
| validation_steps = <span class="fn">max</span>(<span class="nb">1</span>, <span class="fn">int</span>(np.<span class="fn">ceil</span>(n_val / BATCH_SIZE))) |
|
|
| history = model.<span class="fn">fit</span>( |
| train_ds, |
| validation_data=val_ds, |
| epochs=EPOCHS, |
| steps_per_epoch=steps_per_epoch, |
| validation_steps=validation_steps, |
| verbose=<span class="nb">1</span>, |
| ) |
|
|
| save_path = <span class="st">f"{model_type}_best.keras"</span> |
| model.<span class="fn">save</span>(save_path) |
| <span class="fn">print</span>(<span class="st">f"✔ {model_type} saved → {save_path}"</span>) |
| <span class="kw">return</span> model, history</pre> |
| </div> |
|
|
| <div class="steps"> |
| <div class="step"><p>Run all cells from top to bottom to confirm there are no errors.</p></div> |
| <div class="step"><p>Confirm the output shows three training runs (M1, M2, M3) completing with no <code>NameError</code>.</p></div> |
| </div> |
| </div> |
| </div> |
| </section> |
|
|
| |
| <section id="fix2"> |
| <div class="fix-card critical"> |
| <div class="fix-header"> |
| <div class="fix-num">2</div> |
| <div class="fix-title">Replace Synthetic Toy Data with Real CASIA v2</div> |
| </div> |
| <div class="fix-body"> |
| <div class="problem-box"> |
| <div class="label">Problem</div> |
| Current dataset: <strong>Authentic</strong> = random noise (RGB 100–200) | |
| <strong>Forged</strong> = same noise + a solid red rectangle at [50–150, 50–150]. |
| The model learns "red rectangle = forged" and gets 100% accuracy — this has |
| <strong>no relationship to real image forgery detection</strong>. |
| </div> |
| <div class="why-box"> |
| <strong>Why it matters:</strong> An examiner will immediately recognise that 100% accuracy on |
| 120 synthetic noise images is not a valid result. It is the single biggest weakness in the submission. |
| </div> |
|
|
| <div class="substep-heading">Step 2a — Download CASIA v2</div> |
| <div class="steps"> |
| <div class="step"><p>Go to <strong>Kaggle</strong> and search for <em>"CASIA v2 image forgery"</em> or <em>"CASIA 2.0 dataset"</em>.</p></div> |
| <div class="step"><p>Download the dataset (~3.3 GB). It contains ~12,614 authentic images (<code>Au_*</code>) and ~5,123 tampered images (<code>Tp_*</code>).</p></div> |
| <div class="step"><p>Upload the extracted folder to your <strong>Google Drive</strong> and name it <code>casia_v2/</code>.</p></div> |
| </div> |
|
|
| <div class="substep-heading">Step 2b — Mount Drive and Update Data Path</div> |
| <div class="steps"> |
| <div class="step"><p>Add this cell at the top of the data section in the notebook:</p></div> |
| </div> |
| <div class="code-wrap"> |
| <div class="code-label">Python — Mount Google Drive</div> |
| <pre><span class="kw">from</span> google.colab <span class="kw">import</span> drive |
| drive.<span class="fn">mount</span>(<span class="st">'/content/drive'</span>) |
|
|
| TARGET_DIR = <span class="st">"/content/drive/MyDrive/casia_v2"</span> <span class="cm"># adjust if needed</span></pre> |
| </div> |
| <div class="steps"> |
| <div class="step"><p>Comment out or remove the call to <code>generate_robust_dataset()</code> — synthetic data is no longer needed.</p></div> |
| <div class="step"><p>Run <code>split_dataset(TARGET_DIR)</code> directly on the real data path.</p></div> |
| </div> |
|
|
| <div class="substep-heading">Step 2c — Verify the Split</div> |
| <div class="code-wrap"> |
| <div class="code-label">Python — Verify split counts and label balance</div> |
| <pre>splits = <span class="fn">split_dataset</span>(TARGET_DIR) |
| <span class="fn">print</span>(<span class="st">f"Train: {len(splits['train'])} | Val: {len(splits['val'])} | Test: {len(splits['test'])}"</span>) |
|
|
| <span class="kw">for</span> split_name, paths <span class="kw">in</span> splits.items(): |
| authentic = <span class="fn">sum</span>(<span class="nb">1</span> <span class="kw">for</span> p <span class="kw">in</span> paths <span class="kw">if</span> os.path.<span class="fn">basename</span>(p).<span class="fn">startswith</span>(<span class="st">'Au_'</span>)) |
| forged = <span class="fn">sum</span>(<span class="nb">1</span> <span class="kw">for</span> p <span class="kw">in</span> paths <span class="kw">if</span> os.path.<span class="fn">basename</span>(p).<span class="fn">startswith</span>(<span class="st">'Tp_'</span>)) |
| <span class="fn">print</span>(<span class="st">f"{split_name}: {authentic} authentic, {forged} forged"</span>)</pre> |
| </div> |
| <div class="expected"> |
| <div class="exp-label">Expected Output (approximate)</div> |
| Train: ~14000 | Val: ~1700 | Test: ~1700 |
| train: ~10000 authentic, ~4000 forged |
| </div> |
|
|
| <div class="substep-heading">Step 2d — Handle Class Imbalance</div> |
| <div class="callout"> |
| CASIA v2 has ~2.5× more authentic than tampered images. Without class weights, |
| the model will bias toward predicting "authentic" and appear to have high accuracy while missing most forgeries. |
| </div> |
| <div class="code-wrap"> |
| <div class="code-label">Python — Compute class weights</div> |
| <pre><span class="kw">from</span> sklearn.utils.class_weight <span class="kw">import</span> compute_class_weight |
|
|
| classes = np.<span class="fn">unique</span>(train_labels) |
| weights = <span class="fn">compute_class_weight</span>(<span class="st">'balanced'</span>, classes=classes, y=train_labels) |
| class_weight_dict = <span class="fn">dict</span>(<span class="fn">zip</span>(classes, weights)) |
| <span class="fn">print</span>(<span class="st">"Class weights:"</span>, class_weight_dict) |
| <span class="cm"># Then pass class_weight=class_weight_dict to model.fit()</span></pre> |
| </div> |
|
|
| <div class="substep-heading">Step 2e — Retrain and Save</div> |
| <div class="steps"> |
| <div class="step"><p>Run training. Expect accuracy in the range <strong>80–92%</strong> (not 100%). If above 95%, check for data leakage. If below 70%, increase <code>EPOCHS</code> to 10–15.</p></div> |
| <div class="step"><p>Download the trained model from Colab:</p></div> |
| </div> |
| <div class="code-wrap"> |
| <div class="code-label">Python — Download model from Colab</div> |
| <pre><span class="kw">from</span> google.colab <span class="kw">import</span> files |
| files.<span class="fn">download</span>(<span class="st">'M3_best.keras'</span>)</pre> |
| </div> |
| <div class="steps"> |
| <div class="step"><p>Replace the existing <code>M3_best.keras</code> in the repo. Git LFS will handle the large file upload automatically on the next commit.</p></div> |
| </div> |
| </div> |
| </div> |
| </section> |
|
|
| |
| <div class="section-header"> |
| <span class="pill high">High Priority</span> |
| <hr> |
| </div> |
|
|
| |
| <section id="fix3"> |
| <div class="fix-card high"> |
| <div class="fix-header"> |
| <div class="fix-num">3</div> |
| <div class="fix-title">Reorder Notebook Sections</div> |
| </div> |
| <div class="fix-body"> |
| <div class="problem-box"> |
| <div class="label">Problem</div> |
| Section headings appear in the wrong order: <strong>§9</strong> (Execute) appears before |
| <strong>§7</strong> (Explainability) and <strong>§8</strong> (Interface). The notebook is |
| hard to follow and looks unpolished for submission. |
| </div> |
| <div class="steps"> |
| <div class="step"><p>Open the notebook in Colab and rearrange cells into this order:</p></div> |
| </div> |
| <table class="model-table" style="margin:14px 0;"> |
| <thead><tr><th>Section</th><th>Content</th></tr></thead> |
| <tbody> |
| <tr><td>§1</td><td>Setup & Dependencies</td></tr> |
| <tr><td>§2</td><td>Synthetic Dataset Generation <em>(mark as optional — replaced by real data)</em></td></tr> |
| <tr><td>§3</td><td>ELA Utility (<code>compute_ela</code>)</td></tr> |
| <tr><td>§4</td><td>Data Pipeline (CASIAParser, split, preload, make_dataset)</td></tr> |
| <tr><td>§5</td><td>Model Architecture (get_rgb_branch, get_ela_branch, build_model)</td></tr> |
| <tr><td>§6</td><td>Training Engine (<code>run_training</code> — added in Fix 1)</td></tr> |
| <tr><td>§7</td><td>Explainability (<code>get_gradcam</code>)</td></tr> |
| <tr><td>§8</td><td>Interactive Interface (Gradio demo)</td></tr> |
| <tr><td>§9</td><td>Execute: 3-Way Ablation Study</td></tr> |
| <tr><td>§10</td><td>Results & Evaluation <em>(new — see Fix 4)</em></td></tr> |
| </tbody> |
| </table> |
| <div class="steps"> |
| <div class="step"><p>Renumber all section headings to match the table above.</p></div> |
| <div class="step"><p>Run all cells again top-to-bottom to confirm no execution errors.</p></div> |
| </div> |
| </div> |
| </div> |
| </section> |
|
|
| |
| <section id="fix4"> |
| <div class="fix-card high"> |
| <div class="fix-header"> |
| <div class="fix-num">4</div> |
| <div class="fix-title">Add Proper Evaluation Metrics</div> |
| </div> |
| <div class="fix-body"> |
| <div class="problem-box"> |
| <div class="label">Problem</div> |
| Only <code>accuracy</code> is reported. On an imbalanced dataset like CASIA v2, |
| a model that always predicts "authentic" achieves ~71% accuracy while being completely useless. |
| Accuracy alone is not sufficient for a forensics task. |
| </div> |
| <div class="steps"> |
| <div class="step"><p>After the training cell (§9), add a new section <strong>§10 Results & Evaluation</strong>.</p></div> |
| <div class="step"><p>Add this evaluation code to generate a classification report and confusion matrix:</p></div> |
| </div> |
| <div class="code-wrap"> |
| <div class="code-label">Python — Classification report + confusion matrix</div> |
| <pre><span class="kw">from</span> sklearn.metrics <span class="kw">import</span> ( |
| confusion_matrix, classification_report, |
| roc_auc_score, RocCurveDisplay |
| ) |
| <span class="kw">import</span> matplotlib.pyplot <span class="kw">as</span> plt |
| <span class="kw">import</span> seaborn <span class="kw">as</span> sns |
|
|
| test_ds_m3 = <span class="fn">adapt_dataset_for_model</span>( |
| <span class="fn">make_dataset</span>(test_rgb, test_ela, test_labels, repeat=<span class="kw">False</span>), <span class="st">'M3'</span> |
| ) |
| y_pred_prob = model_m3.<span class="fn">predict</span>(test_ds_m3, verbose=<span class="nb">0</span>).<span class="fn">flatten</span>() |
| y_pred = (y_pred_prob > <span class="nb">0.5</span>).astype(<span class="fn">int</span>) |
| y_true = test_labels |
|
|
| <span class="fn">print</span>(<span class="st">"="*50</span>) |
| <span class="fn">print</span>(<span class="st">"M3 (Fused) — Classification Report"</span>) |
| <span class="fn">print</span>(<span class="st">"="*50</span>) |
| <span class="fn">print</span>(<span class="fn">classification_report</span>(y_true, y_pred, |
| target_names=[<span class="st">'Authentic'</span>, <span class="st">'Forged'</span>])) |
|
|
| cm = <span class="fn">confusion_matrix</span>(y_true, y_pred) |
| fig, ax = plt.<span class="fn">subplots</span>(figsize=(<span class="nb">5</span>, <span class="nb">4</span>)) |
| sns.<span class="fn">heatmap</span>(cm, annot=<span class="kw">True</span>, fmt=<span class="st">'d'</span>, cmap=<span class="st">'Blues'</span>, |
| xticklabels=[<span class="st">'Authentic'</span>, <span class="st">'Forged'</span>], |
| yticklabels=[<span class="st">'Authentic'</span>, <span class="st">'Forged'</span>]) |
| ax.<span class="fn">set_xlabel</span>(<span class="st">'Predicted'</span>); ax.<span class="fn">set_ylabel</span>(<span class="st">'Actual'</span>) |
| ax.<span class="fn">set_title</span>(<span class="st">'M3 Confusion Matrix'</span>) |
| plt.<span class="fn">savefig</span>(<span class="st">'confusion_matrix_m3.png'</span>, dpi=<span class="nb">150</span>) |
| plt.<span class="fn">show</span>() |
|
|
| auc = <span class="fn">roc_auc_score</span>(y_true, y_pred_prob) |
| <span class="fn">print</span>(<span class="st">f"ROC-AUC Score: {auc:.4f}"</span>) |
| <span class="fn">RocCurveDisplay</span>.<span class="fn">from_predictions</span>(y_true, y_pred_prob) |
| plt.<span class="fn">title</span>(<span class="st">"M3 ROC Curve"</span>) |
| plt.<span class="fn">savefig</span>(<span class="st">'roc_curve_m3.png'</span>, dpi=<span class="nb">150</span>) |
| plt.<span class="fn">show</span>()</pre> |
| </div> |
| <div class="steps"> |
| <div class="step"><p>Add the model comparison table across all three variants:</p></div> |
| </div> |
| <div class="code-wrap"> |
| <div class="code-label">Python — M1 vs M2 vs M3 metrics table</div> |
| <pre>results = {} |
| <span class="kw">for</span> name, model, m_type <span class="kw">in</span> [(<span class="st">"M1_RGB"</span>, model_m1, <span class="st">'M1'</span>), |
| (<span class="st">"M2_ELA"</span>, model_m2, <span class="st">'M2'</span>), |
| (<span class="st">"M3_Fused"</span>, model_m3, <span class="st">'M3'</span>)]: |
| ds = <span class="fn">adapt_dataset_for_model</span>( |
| <span class="fn">make_dataset</span>(test_rgb, test_ela, test_labels, repeat=<span class="kw">False</span>), m_type) |
| probs = model.<span class="fn">predict</span>(ds, verbose=<span class="nb">0</span>).<span class="fn">flatten</span>() |
| preds = (probs > <span class="nb">0.5</span>).astype(<span class="fn">int</span>) |
| <span class="kw">from</span> sklearn.metrics <span class="kw">import</span> f1_score, precision_score, recall_score |
| results[name] = { |
| <span class="st">'Accuracy'</span>: np.<span class="fn">mean</span>(preds == test_labels), |
| <span class="st">'Precision'</span>: <span class="fn">precision_score</span>(test_labels, preds, zero_division=<span class="nb">0</span>), |
| <span class="st">'Recall'</span>: <span class="fn">recall_score</span>(test_labels, preds, zero_division=<span class="nb">0</span>), |
| <span class="st">'F1'</span>: <span class="fn">f1_score</span>(test_labels, preds, zero_division=<span class="nb">0</span>), |
| <span class="st">'AUC'</span>: <span class="fn">roc_auc_score</span>(test_labels, probs), |
| } |
|
|
| <span class="kw">import</span> pandas <span class="kw">as</span> pd |
| df_results = pd.<span class="fn">DataFrame</span>(results).T |
| <span class="fn">print</span>(<span class="st">"\nAblation Study Results"</span>) |
| <span class="fn">print</span>(df_results.<span class="fn">to_string</span>(float_format=<span class="st">"{:.4f}"</span>.<span class="fn">format</span>))</pre> |
| </div> |
| <div class="steps"> |
| <div class="step"><p>Save <code>confusion_matrix_m3.png</code> and <code>roc_curve_m3.png</code> and include them in the project report.</p></div> |
| </div> |
| </div> |
| </div> |
| </section> |
|
|
| |
| <section id="fix5"> |
| <div class="fix-card high"> |
| <div class="fix-header"> |
| <div class="fix-num">5</div> |
| <div class="fix-title">Make the Ablation Study Meaningful</div> |
| </div> |
| <div class="fix-body"> |
| <div class="problem-box"> |
| <div class="label">Problem</div> |
| With synthetic data, M1=M2=M3=100% — the ablation proves nothing. |
| With real CASIA v2, the three models will produce genuinely different results. |
| <strong>Depends on Fix 2 and Fix 4 being completed first.</strong> |
| </div> |
| <div class="steps"> |
| <div class="step"> |
| <p>After real-data training, expect results similar to this pattern:</p> |
| </div> |
| </div> |
| <table class="model-table"> |
| <thead><tr><th>Model</th><th>Input</th><th>Expected Accuracy</th><th>Strength</th><th>Weakness</th></tr></thead> |
| <tbody> |
| <tr><td>M1 (RGB)</td><td>Original image</td><td>~75–85%</td><td>Semantic inconsistencies</td><td>Misses compression artifacts</td></tr> |
| <tr><td>M2 (ELA)</td><td>ELA residuals</td><td>~70–80%</td><td>Compression tampering</td><td>Misses structural forgeries</td></tr> |
| <tr><td><strong>M3 (Fused)</strong></td><td>Both</td><td><strong>~85–92%</strong></td><td>Combines both signals</td><td>Slightly slower inference</td></tr> |
| </tbody> |
| </table> |
| <div class="steps"> |
| <div class="step"><p>The comparison table from Fix 4 is your ablation study table — no separate code needed.</p></div> |
| <div class="step"><p>Add a markdown cell before the results table explaining why fusion outperforms single-branch models. Use the table above as a guide.</p></div> |
| </div> |
| </div> |
| </div> |
| </section> |
|
|
| |
| <div class="section-header"> |
| <span class="pill medium">Medium Priority</span> |
| <hr> |
| </div> |
|
|
| |
| <section id="fix6"> |
| <div class="fix-card medium"> |
| <div class="fix-header"> |
| <div class="fix-num">6</div> |
| <div class="fix-title">Update <code>train.py</code> to Match the Notebook</div> |
| </div> |
| <div class="fix-body"> |
| <div class="problem-box"> |
| <div class="label">Problem</div> |
| <code>train.py</code> only builds and trains M3. It is missing <code>adapt_dataset_for_model()</code>, |
| <code>run_training()</code>, and M1/M2 variants — all of which exist in the notebook. |
| </div> |
| <div class="steps"> |
| <div class="step"><p>Add <code>adapt_dataset_for_model()</code> from the notebook to <code>train.py</code>.</p></div> |
| <div class="step"><p>Add <code>run_training()</code> (same as Fix 1) to <code>train.py</code>.</p></div> |
| <div class="step"><p>Update <code>build_model()</code> to accept a <code>model_type</code> parameter (<code>'M1'</code>, <code>'M2'</code>, <code>'M3'</code>) matching the notebook version.</p></div> |
| <div class="step"><p>Update the <code>if __name__ == "__main__":</code> block to train all three models and print the ablation comparison table.</p></div> |
| </div> |
| </div> |
| </div> |
| </section> |
|
|
| |
| <section id="fix7"> |
| <div class="fix-card medium"> |
| <div class="fix-header"> |
| <div class="fix-num">7</div> |
| <div class="fix-title">Add the Project Report to the Repository</div> |
| </div> |
| <div class="fix-body"> |
| <div class="problem-box"> |
| <div class="label">Problem</div> |
| <code>README.md</code> references |
| <code>Documents/Project_Report_Digital_Image_Forgery_Detector.docx</code> |
| but neither the file nor the <code>Documents/</code> folder exist in the repo. |
| </div> |
| <div class="steps"> |
| <div class="step"><p>Create the <code>Documents/</code> folder in the repo root.</p></div> |
| <div class="step"><p>Place the project report <code>.docx</code> file inside it.</p></div> |
| <div class="step"><p>Run <code>git add Documents/</code> and commit.</p></div> |
| </div> |
| <div class="callout"> |
| If the report does not yet exist, remove the reference from <code>README.md</code> |
| until it is ready — a broken link is worse than no link. |
| </div> |
| </div> |
| </div> |
| </section> |
|
|
| |
| <section id="fix8"> |
| <div class="fix-card medium"> |
| <div class="fix-header"> |
| <div class="fix-num">8</div> |
| <div class="fix-title">Add a Literature Comparison Section</div> |
| </div> |
| <div class="fix-body"> |
| <div class="problem-box"> |
| <div class="label">Problem</div> |
| The notebook does not reference any published results on CASIA v2, |
| making it impossible for an examiner to judge whether the results are competitive. |
| </div> |
| <div class="steps"> |
| <div class="step"><p>After the results table (§10), add a markdown cell titled <strong>"Comparison with Published Baselines"</strong>.</p></div> |
| <div class="step"><p>Use this template (fill in your actual results after Fix 2 is done):</p></div> |
| </div> |
| <table class="model-table"> |
| <thead><tr><th>Method</th><th>Accuracy</th><th>F1</th><th>Notes</th></tr></thead> |
| <tbody> |
| <tr><td>Rao et al. (2016) — CNN on SRM features</td><td>82.2%</td><td>—</td><td>Single-branch</td></tr> |
| <tr><td>Salloum et al. (2018) — FCN</td><td>89.3%</td><td>—</td><td>Pixel-level</td></tr> |
| <tr><td><strong>Our M1 (RGB only)</strong></td><td><em>your result</em></td><td><em>your result</em></td><td>ResNet50</td></tr> |
| <tr><td><strong>Our M2 (ELA only)</strong></td><td><em>your result</em></td><td><em>your result</em></td><td>Custom CNN</td></tr> |
| <tr><td><strong>Our M3 (Fused)</strong></td><td><em>your result</em></td><td><em>your result</em></td><td>Dual-branch</td></tr> |
| </tbody> |
| </table> |
| <div class="steps"> |
| <div class="step"><p>Add 2–3 sentences commenting on whether M3 is competitive and why it may be higher or lower than the baselines.</p></div> |
| </div> |
| </div> |
| </div> |
| </section> |
|
|
| |
| <div class="section-header"> |
| <span class="pill low">Low Priority</span> |
| <hr> |
| </div> |
|
|
| |
| <section id="fix9"> |
| <div class="fix-card low"> |
| <div class="fix-header"> |
| <div class="fix-num">9</div> |
| <div class="fix-title">Align <code>get_gradcam()</code> in <code>app.py</code> with the Notebook</div> |
| </div> |
| <div class="fix-body"> |
| <div class="problem-box"> |
| <div class="label">Problem</div> |
| The notebook's <code>get_gradcam()</code> uses a <code>model_type</code> parameter to pick |
| the correct last conv layer per model variant. The <code>app.py</code> version only searches |
| for <code>conv2d</code> named layers — if the model is updated it could silently pick the wrong layer. |
| </div> |
| <div class="steps"> |
| <div class="step"><p>In <code>app.py</code>, replace the current <code>get_gradcam()</code> with the more robust version from the notebook.</p></div> |
| <div class="step"><p>Since <code>app.py</code> only runs M3, hard-code the call as:</p></div> |
| </div> |
| <div class="code-wrap"> |
| <div class="code-label">Python — Updated call in app.py</div> |
| <pre>heatmap = <span class="fn">get_gradcam</span>(m3, input_data, model_type=<span class="st">'M3'</span>)</pre> |
| </div> |
| </div> |
| </div> |
| </section> |
|
|
| |
| <section id="fix10"> |
| <div class="fix-card low"> |
| <div class="fix-header"> |
| <div class="fix-num">10</div> |
| <div class="fix-title">Document the Gradio → Streamlit Difference in README</div> |
| </div> |
| <div class="fix-body"> |
| <div class="problem-box"> |
| <div class="label">Problem</div> |
| The notebook uses <strong>Gradio</strong> (Colab-native); the deployed app uses |
| <strong>Streamlit</strong> (Hugging Face Spaces). This intentional difference |
| is not explained anywhere and may confuse an examiner. |
| </div> |
| <div class="steps"> |
| <div class="step"><p>Add the following section to <code>README.md</code>:</p></div> |
| </div> |
| <div class="code-wrap"> |
| <div class="code-label">Markdown — README.md addition</div> |
| <pre>## Development vs Deployment UI |
|
|
| The Colab notebook uses **Gradio** for its interactive demo because Gradio |
| works natively within Colab with a public share link. |
| The deployed Hugging Face Space uses **Streamlit** because it is the SDK |
| configured in the Space settings. |
| Both interfaces implement identical inference logic.</pre> |
| </div> |
| </div> |
| </div> |
| </section> |
|
|
| |
| <section id="order" style="margin-top:48px;"> |
| <h2 style="font-size:20px;font-weight:800;margin-bottom:16px;color:#0f172a;">Recommended Implementation Order</h2> |
| <p style="color:#64748b;font-size:14px;margin-bottom:20px;">Work through the fixes in this order to avoid rework — later fixes depend on earlier ones.</p> |
| <div class="order-flow"> |
| <div class="order-step">Fix 2a–b <span>Download CASIA v2</span></div> |
| <span class="order-arrow">→</span> |
| <div class="order-step">Fix 1 <span>run_training()</span></div> |
| <span class="order-arrow">→</span> |
| <div class="order-step">Fix 3 <span>Reorder notebook</span></div> |
| <span class="order-arrow">→</span> |
| <div class="order-step">Fix 2c–e <span>Retrain on real data</span></div> |
| <span class="order-arrow">→</span> |
| <div class="order-step">Fix 4 <span>Eval metrics</span></div> |
| <span class="order-arrow">→</span> |
| <div class="order-step">Fix 5 <span>Ablation write-up</span></div> |
| <span class="order-arrow">→</span> |
| <div class="order-step">Fix 6 <span>train.py parity</span></div> |
| <span class="order-arrow">→</span> |
| <div class="order-step">Fix 7 <span>Project report</span></div> |
| <span class="order-arrow">→</span> |
| <div class="order-step">Fix 8 <span>Literature</span></div> |
| <span class="order-arrow">→</span> |
| <div class="order-step">Fix 9 <span>Grad-CAM</span></div> |
| <span class="order-arrow">→</span> |
| <div class="order-step">Fix 10 <span>README</span></div> |
| </div> |
| </section> |
|
|
| |
| <section id="done" style="margin-top:48px;"> |
| <h2 style="font-size:20px;font-weight:800;margin-bottom:6px;color:#0f172a;">Definition of Done</h2> |
| <p style="color:#64748b;font-size:14px;margin-bottom:20px;">Tick each item off as you complete it. Submission is ready only when all boxes are checked.</p> |
| <ul class="checklist"> |
| <li><input type="checkbox" id="c1"><label for="c1">Notebook runs end-to-end in Colab without errors (no missing functions)</label></li> |
| <li><input type="checkbox" id="c2"><label for="c2">Notebook sections are numbered and ordered correctly (§1 through §10)</label></li> |
| <li><input type="checkbox" id="c3"><label for="c3">Training uses real CASIA v2 data — not synthetic noise</label></li> |
| <li><input type="checkbox" id="c4"><label for="c4">Accuracy is in a realistic range (75–92%) — <strong>not 100%</strong></label></li> |
| <li><input type="checkbox" id="c5"><label for="c5">Class imbalance is handled via class weights in <code>model.fit()</code></label></li> |
| <li><input type="checkbox" id="c6"><label for="c6">Evaluation section includes: confusion matrix, precision, recall, F1, ROC-AUC</label></li> |
| <li><input type="checkbox" id="c7"><label for="c7">Ablation study table compares M1, M2, M3 across all metrics</label></li> |
| <li><input type="checkbox" id="c8"><label for="c8">Literature comparison table present with at least two published baselines</label></li> |
| <li><input type="checkbox" id="c9"><label for="c9"><code>train.py</code> trains all three models (M1, M2, M3)</label></li> |
| <li><input type="checkbox" id="c10"><label for="c10"><code>M3_best.keras</code> in the repo was trained on real CASIA v2 data</label></li> |
| <li><input type="checkbox" id="c11"><label for="c11"><code>Documents/</code> folder contains the project report</label></li> |
| <li><input type="checkbox" id="c12"><label for="c12"><code>README.md</code> explains the Gradio vs Streamlit difference</label></li> |
| <li><input type="checkbox" id="c13"><label for="c13">Hugging Face Space redeployed with the new model weights</label></li> |
| </ul> |
| </section> |
|
|
| </main> |
|
|
| </body> |
| </html> |
|
|
